the development of mechatronic machine vision system for inspection of ceramic plates
DESCRIPTION
This is my Master final thesis for the conducted research that led to build a mechatronic machine vision system for inspection of garnished wall plates.TRANSCRIPT
Ain Shams University
Faculty f Engineering
DESIGN AND IMPLEMENTATION OF FLEXIBLE
MANUFACTURING CELL FOR THE
INSPECTION OF CERAMIC WALL PLATES
A Thesis Submitted in Partial Fulfilment of the Requirements
for the Degree of MSc. In Mechatronics
By
Waleed Abd El-Megeed El-Badry
BSc. In Mechanical Engineering (Mechatronics Branch)
Teaching Assistant, Mechatronics Department
College of Engineering
Misr University for Science and Technology
Supervised By
Prof. Farid A. Tolbah
Emeritus Professor in Design and Production Engineering Department
Ain Shams University
Dr. Ahmed M. Aly
Assistant Professor in Design and Production Engineering Department
Ain Shams University
2012
II
ABSTRACT
Quality inspection is one of the recurring topics among industrial
community. Garnished wall plates may require inspection in terms of
geometry (height, width, corner radius ...etc), Drawn Objects Orientation
(Angles with respect to reference axis) and colour of drawn objects.
Emulation of human decision in classification is what inspection is all
about.
In this thesis, we implemented a hybrid system of fuzzy logic and
fuzzy c-mean clustering for quality inspection of garnished ceramic wall
plates. This novel approach is designed to inspect colour, geometry, angles
and existence of colour spots. The study is intended to reduce error due to
human visual inspection. A developed Flexible Manufacturing Cell (FMC)
is responsible for inspection of ceramic plates applying Computer Vision
System (CVS) and automation of the cell. The bench marking of the
proposed algorithm is considered promising compared to other published
peers.
III
SUMMARY
The research effort expended upon the problem of objectively
inspecting, analysing and characterizing garnished ceramic tiles is easily
justified by the commercial and safety benefits to the industry such as
automating obsolete manual inspection procedure, providing higher
homogeneity within sorted grades which consequently would improve the
processing stability of the overall inspection procedure.
Tiredness and lack of concentration are common problems, leading
to errors in grading tiles. Gradual changes are difficult for human
inspectors to detect and it is possible that slight and progressive changes
will not be noticed at an early stage. Additionally, it is particularly difficult
for the human eye to accurately sort tiles into shades, with changing light
conditions in a factory. Human judgment is, as usual, influenced by
expectations and prior knowledge.
Since early 90s, several published papers were concerned with
finding new approaches to detect cracks and colour spots in ceramic tiles.
However, most of them were targeting the inspection algorithm and
neglected the other aspects of machine vision system (variability in
illumination and surface reflectivity, image acquisition, mechanical
handling system and electrical interfacing circuit). Meanwhile, measuring
drawn objects geometry and colour quality has never been discussed prior
to the conducted research which is demanded when inspecting garnished
wall plates.
The proposed thesis offers a novel approach in performing
geometric and colour quality inspection based on a developed hybrid
technique of fuzzy logic and fuzzy c-mean clustering to detect cracks,
colour spots and colour mismatch. It also offers scoring for extracted
geometric and colour features.
Live image acquisition parameters are discussed in details with
different parameters affecting the quality of acquired image such as
gamma correction and frame rate consideration. Detailed steps for live
acquisition are also discussed.
A proposed algorithm for image calibration to minimise lens
distortion is demonstrated. The proposed algorithm proved its validity in
lens and perspective correction.
IV
To perform colour grouping, fuzzy c-clustering was chosen where
a visual representation of clustering is displayed to offer visual
interpretation of drawn objects isolation rather than dummy clustering
nodes.
Geometric feature extraction takes place by means of edge
detection using tuned Prewit filter with rays of spokes and line fitting for
evaluation of edge length. Moreover, geometric and colour scores are
calculated based on the metrics measured.
A fuzzy classifier having two inputs (geometry and colour score)
and three outputs (acceptable, colour spot and colour mismatch) were built
for garnished ceramic grading.
A test rig was designed and manufactured for evaluation of the
proposed system. The evaluation of the system showed promising results
in terms of detection accuracy of correct ceramic grade. The effect of
ceramic orientation was also studied to demonstrate the efficiency of the
proposed calibration algorithm. Meanwhile, a benchmarking was carried
out to assess the overall performance. A comparative study was performed
to show the effect of utilization of fuzzy c-mean over the nearest neighbour
approach which showed a better influence in terms of correct pattern
detection by using the latter technique.
Parts of the presented work in the submitted thesis were published
in [1-3].
V
ACKNOWLEDGEMENT
The researcher is obliged to his supervisors, Prof. Farid Tolbah and
Dr. Ahmed Aly for their recurring support and guiding during the research
period, He also carries gratitude to the venerable referees, namely Prof.
Magdy Abdel Hameed and Prof. Mohammed El-Adawy for their kind
hospitality and comments to enhance this research.
The priceless help of Prof. Mustafa Eissa in reviewing and
enhancing this thesis will be memorable for life.
The conducted research would not have been carried out without
the devoting assistance from Eng. Mahmoud Hassan who had major
fingerprints in the mechanical design of the built flexible cell.
VI
TABLE OF CONTENTS
LITERATURE REVIEW ....................................................................................... 1
1.1 INTRODUCTION .................................................................................................. 1
1.2 MACHINE VISION AND COMPUTER VISION .............................................................. 3
1.3 MACHINE VISION AND HUMAN VISION ................................................................... 6
1.4 INSPECTION OF CERAMIC PLATES ............................................................................ 6
1.5 CHALLENGES IN AUTOMATED INSPECTION ............................................................... 7
1.6 STATEMENT OF THE PROBLEM ............................................................................. 15
1.7 OBJECTIVE OF THE PRESENT WORK ....................................................................... 15
IMAGE ACQUISITION AND PRE-PROCESSING ................................................ 17
2.1 SPATIAL CALIBRATION ....................................................................................... 17
2.1.1 Lens distortion ....................................................................................... 18
2.1.2 Telecentric Lens ..................................................................................... 18
2.1.3 Software based correction .................................................................... 19
2.2 PROPOSED ALGORITHM FOR CORRECTING AND CALIBRATING LENS DISTORTION ............. 20
2.2.1 Colour plane extraction ......................................................................... 20
2.2.2 Automatic Thresholding ........................................................................ 21
2.2.3 Particle grouping ................................................................................... 23
2.2.4 Particles and distance measurements................................................... 24
2.2.5 Performing correction on each pixel ..................................................... 26
2.3 IMAGE ACQUISITION .......................................................................................... 29
2.3.1 Analogue cameras................................................................................. 29
2.3.2 Digital Cameras ..................................................................................... 30 2.3.2.1 Camera Link ........................................................................................ 31 2.3.2.2 USB cameras ....................................................................................... 31 2.3.2.3 IEEE 1394 (Firewire cameras) .............................................................. 31
2.4 ACQUISITION PARAMETERS ................................................................................. 32
2.4.1 Image format ........................................................................................ 32
2.4.2 Speed ..................................................................................................... 33
2.4.3 Bayer colour filter .................................................................................. 33
2.4.4 Brightness and contrast ........................................................................ 35
2.4.5 Gain ....................................................................................................... 36
2.5 GAMMA CORRECTION ....................................................................................... 37
SOFT PARTITIONING AND COLOUR GROUPING ............................................. 39
3.1 CLUSTERING .................................................................................................... 39
3.2 PROBLEM OF CLUSTERING .................................................................................. 40
3.3 ADVANTAGES OF USING CLUSTERING .................................................................... 40
3.4 CONSTRAINTS OF USING CLUSTERING .................................................................... 40
VII
3.5 METHODOLOGIES OF DISTANCE MEASUREMENTS .................................................... 41
3.5.1 Common distance functions .................................................................. 41 3.5.1.1 The Euclidean distance ....................................................................... 41 3.5.1.2 The Manhattan distance (aka taxicab norm or 1-norm) ..................... 41 3.5.1.3 The Hamming distance ....................................................................... 42
3.6 CLUSTERING TECHNIQUES .................................................................................. 43
3.7 EFFECTIVENESS OF COLOUR CLUSTERING ............................................................... 44
3.7.1 The need for fuzzification in colour grouping ........................................ 44
3.7.2 Challenges in fuzzy clustering ............................................................... 44
3.7.3 Proposed soft partitioning and group visualization using fuzzy
clustering (fuzzy c-mean) ................................................................................... 45
GEOMETRIC FEATURE EXTRACTION .............................................................. 49
4.1 DEFINITION OF FEATURE VECTOR ......................................................................... 49
4.2 FEATURES OF INTEREST IN GARNISHED WALL PLATES ................................................ 50
4.3 APPLIED ALGORITHM FOR PRELIMINARY LOCATING OF DRAWN OBJECTS ....................... 50
Methodology of edge detection ............................................................ 50
Formulating edge detectors .................................................................. 51
Tuned Prewitt Filter ............................................................................... 53
4.4 COMPUTING FEATURE SCORE ............................................................................. 55
Calculating the score for colour quality ................................................ 55
Calculating the accuracy of geometric features .................................... 55
CLASSIFICATION OF CERAMIC PLATES USING FUZZY LOGIC ........................... 58
FUZZY LOGIC AND FUZZY LOGIC SYSTEMS ............................................................... 58
MAMDANI’S FUZZY LOGIC SYSTEM ....................................................................... 60
5.2.1 Fuzzification .......................................................................................... 60
5.2.2 Rules ...................................................................................................... 61 5.2.2.1 Fuzzy operations ................................................................................. 61 5.2.2.2 Implication .......................................................................................... 63 5.2.2.3 Aggregation ......................................................................................... 63
5.2.3 Defuzzification ....................................................................................... 64 5.2.3.1 Defuzzification techniques .................................................................. 65
METHODOLOGIES UTILIZED IN BUILDING A FUZZY INFERENCE SYSTEM .......................... 65
FORMULATING THE PROPOSED FUZZY CLASSIFIER FOR THE ASSESSMENT OF GARNISHED
WALL PLATES ................................................................................................................ 67
5.4.1 Fuzzification of input and output .......................................................... 68
5.4.2 Rules for classification ........................................................................... 69
5.4.3 Proposed modified defuziification method ........................................... 70
RESULTS AND DISCUSSION ........................................................................... 71
6.1 IMPLEMENTATION OF THE PROPOSED SYSTEM ............................................................... 71
VIII
6.1.1 Mechanical Design .................................................................................... 71
6.1.2 Electrical Circuitry ..................................................................................... 72
6.1.3 Software Architecture ............................................................................... 74
6.2 RESULTS ................................................................................................................ 75
6.3 DISCUSSION ........................................................................................................... 79
CONCLUSION ................................................................................................ 81
FUTURE WORK .............................................................................................. 82
REFERENCES ................................................................................................. 83
PUBLISHED PAPERS ....................................................................................... 89
IX
LIST OF FIGURES
FIGURE 1.1 MECHATRONICS ENGINEERING DISCIPLINE .................................................................. 1
FIGURE 1.2 ARCHETYPAL MACHINE VISION SYSTEMS APPLICABLE TO A WIDE ..................................... 2
FIGURE 2.1 NOTION OF IMAGE CALIBRATION ............................................................................ 17
FIGURE 2.2 TYPES OF LENS DISTORTION ................................................................................... 18
FIGURE 2.3 IN A TELECENTRIC SYSTEM RAYS GET INTO THE OPTICS ONLY ......................................... 19
FIGURE 2.4 THE PROPOSED ALGORITHM FOR PERSPECTIVE CORRECTION ......................................... 20
FIGURE 2.5 SCREENSHOT OF CALIBRATION GRID USED IN CORRECTION AFTER RED PLANE EXTRACTION .. 21
FIGURE 2.6 ADAPTIVE THRESHOLDING USING CLUSTERING TECHNIQUE ........................................... 22
FIGURE 2.7 RESULTED IMAGE OF BOUNDARY TRACKING OF GRID DOTS ........................................... 23
FIGURE 2.8 DETECTING CENTRES TO CHECK FOR SKEWNESS OF EACH DOT ....................................... 25
FIGURE 2.9 VISUALIZATION OF DETECTED DOTS IN THE GRID ........................................................ 25
FIGURE 2.10 PIXEL MAPPING TO RESTORE EXPECTED GRIDS LOCATIONS .......................................... 26
FIGURE 2.11 ACQUIRED IMAGE OF CERAMIC PLATE .................................................................... 27
FIGURE 2.12 THE CORRECTED IMAGE AFTER APPLYING THE ALGORITHM ......................................... 28
FIGURE 2.13 SCREENSHOT OF THE DEVELOPED SOFTWARE FOR CALIBRATION OF CERAMIC PLATE ......... 28
FIGURE 2.14 CCD ARRAY CONFIGURATION .............................................................................. 30
FIGURE 2.15 IMAGE FORMAT AND DELIVERED SPEED RATE .......................................................... 33
FIGURE 2.16 BAYER FILTER (CONCEPT AND CONFIGURATION)....................................................... 34
FIGURE 2.17 BRIGHTNESS ADJUSTMENT .................................................................................. 35
FIGURE 2.18 GAIN CONTROL ................................................................................................. 36
FIGURE 2.19 GAMMA CORRECTION OF THE ACQUIRED CERAMIC IMAGE (Γ=0.7) .............................. 37
FIGURE 2.20 IMPLEMENTATION OF GAMMA CORRECTION .......................................................... 38
FIGURE 3.1 CLUSTERING IS PERFORMED BY GROUPING EACH SIMILAR DATA BASED ON COMMON CRITERIA
........................................................................................................................... 39
FIGURE 3.2 TAXICAB GEOMETRY VS. EUCLIDEAN DISTANCE .......................................................... 42
FIGURE 3.3 EXAMPLE OF HAMMING DISTANCE .......................................................................... 42
FIGURE 3.4 VISUAL REPRESENTATION OF CLUSTERING RESULT ON THE GARNISHED WALL PLATES .......... 46
FIGURE 3.5 THE DEVELOPED SOFTWARE SHOWING FUZZY CLUSTERING EFFECT IN HORIZONTAL
ORIENTATION ......................................................................................................... 47
FIGURE 3.6 THE DEVELOPED SOFTWARE SHOWING FUZZY CLUSTERING EFFECT IN VERTICAL ORIENTATION
........................................................................................................................... 48
FIGURE 4.1 EXTRACTING GEOMETRIC FEATURES ........................................................................ 49
FIGURE 4.2 FEATURE VECTOR AS METRICS FOR INSPECTION.......................................................... 49
FIGURE 4.3 PROCESSED PIXEL AND ITS NEIGHBOURS ................................................................... 51
FIGURE 4.4 KERNEL MULTIPLIED BY THE IMAGE WINDOW ............................................................ 51
FIGURE 4.5 EDGE DETECTION AND EXTRACTING GEOMETRY OF DRAWN OBJECTS .............................. 53
FIGURE 4.6 FITTING DETECTED POINTS ALONG THE PROFILE INTO LINES .......................................... 54
FIGURE 4.7 SCREENSHOT OF THE DEVELOPED SOFTWARE SHOWING THE FEATURE EXTRACTION STAGE .. 56
FIGURE 5.1 AN EAMPLE OF MEMBERSHIP FUNCRION FOR EVALUATION OF A PROGRAM ..................... 59
X
FIGURE 5.2 FUZZIFYING CERAMIC PLATES FEATURES VECTOR ........................................................ 61
FIGURE 5.3 EXAMPLE OF IMPLEMENTING FUZZY LOGICAL OPERATIONS ........................................... 62
FIGURE 5.4 AN IMPLICATION EXAMPLE FROM THE PROPOSED RULES .............................................. 63
FIGURE 5.5 EXAMPLE OF AGGREGATING EFFECT ON THE POSSIBILITY OF ACCEPTING THE CERAMIC PLATE
........................................................................................................................... 64
FIGURE 5.6 RESULT OF DIFFERENT DEFUZZIFICATION TECHNIQUES ................................................ 66
FIGURE 5.7 FUZZY LOGIC SYSTEM .......................................................................................... 67
FIGURE 5.8 SCREENSHOT OF THE DEVELOPED MAMDANI’S INFERENCE ENGINE FOR CERAMIC ASSESSMENT
........................................................................................................................... 67
FIGURE 5.9 COLOUR SCORE MEMBERSHIP FUNCTION .................................................................. 68
FIGURE 5.10 GEOMETRIC SCORE MEMBERSHIP FUNCTION ........................................................... 68
FIGURE 5.11 THE OUTPUT MEMBERSHIP FUNCTIONS OF CERAMIC GRADING .................................... 69
FIGURE 6.1 THE TEST RIG OF THE PROPOSED SYSTEM .................................................................. 71
FIGURE 6.2 THE H-BRIDGE CIRCUIT ......................................................................................... 72
FIGURE 6.3 SCHEMATIC DIAGRAM OF THE USB INTERFACING CARD ............................................... 73
FIGURE 6.4 PCB LAYOUT OF THE INTERFACING CARD .................................................................. 73
FIGURE 6.5 ARCHITECTURAL DESIGN OF THE DEVELOPED SOFTWARE .............................................. 74
FIGURE 6.6 ROTATED GARNISHED CERAMIC PLATE ..................................................................... 75
FIGURE 6.7 CERAMIC PLATE WITH COLOUR MISMATCH ............................................................... 75
FIGURE 6.8 CERAMIC PLATE WITH COLOUR SPOTS ...................................................................... 76
FIGURE 6.9 THE EFFECT OF ROTATING CERAMIC PLATES OVER CORRECT DETECTION OF ITS CLASS ......... 77
FIGURE 6.10 DETECTION PERCENTAGE OF CERAMIC GRADE (NEAREST NEIGBOUR VS. FUZZY C-MEAN .... 78
XI
LIST OF TABLES
TABLE 1.1 THE DIFFERENCE BETWEEN MACHINE VISION AND COMPUTER VISION ................................ 5
TABLE 2.1 COMPARISON OF AVAILABLE CAMERAS UTILIZED IN MACHINE VISION APPLICATION ............. 32
TABLE 5.1 RANGE OF COLOUR SCORE ...................................................................................... 68
TABLE 5.2 RANGE OF GEOMETRIC SCORE ................................................................................. 68
TABLE 5.3 RULES FOR CLASSIFYING GARNISHED PLATES ............................................................... 69
TABLE 6.1 RESULT OF THE PROPOSED CLASSIFICATION SYSTEM FOR THE STATED FIGURES ................... 76
TABLE 6.2 ACCURACY OF AUTOMATIC DETECTION OF CORRECT CLASSES ......................................... 77
TABLE 6.3 SELECTED MTERICS FOR SOFTWARE PERFORMANCE ASSESSMENT .................................... 79
TABLE 6.4 SYSTEM CONFIGURATION ....................................................................................... 79
XII
LIST OF PSEUDO CODE
LIST 2.1 COLOUR PLANE EXTRACTION ALGORITHM ..................................................................... 21
LIST 2.2 PSEUDO OF THRESHOLDING USING CLUSTERING ALGORITHM ............................................ 23
LIST 2.3 BOUNDARY EXTRACTION OF GRIDS CONTOUR ................................................................ 24
LIST 2.4 RESTORATION OF GRIDS CIRCULAR SHAPE ..................................................................... 26
LIST 2.5 RETRIEVAL OF GRIDS CORRECTED LOCATIONS ................................................................. 26
LIST 3.1 COLOUR SORTING WITH FUZZY C-MEAN ........................................................................ 46
LIST 3.2 THE ALGORITHM FOR GROUPING OBJECTS BY COLOUR AND LOCATION ................................ 47
XIII
LIST OF ABBREVIATIONS AND ACRONYMS
Aka Also Known As
API Application Programming
Interfacing
ASSIST Automatic System for Surface
Inspection and Sorting of Tiles
BMU Best Matching Unit
CCD Charge Coupled Device
CFA Colour Filter Array
CS Colour Score
CV Computer Vision
DOM Degree Of Membership
DS Dimension Score
DTS Distance Score
FCM Fuzzy C-Mean
FIS Fuzzy Inference System
FIS Fuzzy Inference System
FL Fuzzy Logic
FLS Fuzzy Logic System
FMC Flexible Manufacturing Cell
FMS Flexible Manufacturing System
IPT Interactive Prototype Toolkit
LOM Largest Of Maximum
MF Membership Function
MOM Middle Of Maximum
MV Machine Vision
OGS Overall Geometry Score
OS Orientation Score
RGB Red ,Green and Blue colour planes
ROI Region Of Interest
SOM Self-Organising Maps.
SOM Smallest Of Maximum
TM Target Machine
TMPLAR Template Learning from Atomic
Representations
VB Visual Basic
Vs Versus
1
Literature Review
1.1 Introduction
Mechatronics is an integration of mechanical engineering,
electrical engineering, automatic control and computer science[4]. This
discipline gains momentum among industrial community since researchers
in this field became involved in robotics, automated measurements and
machine vision (Figure 1.1).
Figure 1.1 Mechatronics engineering discipline
Machine vision (MV) as shown in Figure 1.2 is concerned with the
engineering of integrated mechanical-optical-electronic-software systems
for examining objects and materials, human artefacts and manufacturing
processes, in order to detect defects and improve quality, operating
efficiency and the safety of both products and processes.
In addition to inspection, machine vision can be used to control the
machines used in manufacturing and material processing[5]. These may
perform such operations as cutting, trimming, grasping, manipulating,
packing, assembling, painting, decorating, coating, welding…. etc [6].
•Software engineering
• Code reusability
• Hardware interfacing APIs.
•Dynamic response
• Intelligent control
• System stability
•Interfacing with elctronic preipherals
• Signal conditioninig
• Signal transmission
• Handling Units
• Conveyors
• Stress Analysis
Mechanical Engineering
Electrical Engineering
Computer Science
Automatic Control
2
Automated visual inspection systems allow manufacturers to
monitor and control product quality, thus maintaining/enhancing their
competitive position. Machine vision is also being used to ensure greater
safety and reliability of manufacturing processes.
Figure 1.2 Archetypal Machine Vision systems applicable to a wide
The confidence being gained by applying machine vision to
engineering manufacturing is now spilling over into industries such as
food processing, agriculture, horticulture, textile manufacturing, etc.,
where product variability is intrinsically higher.
3
Thus, we are at the threshold of what we predict will be a period of
rapid growth of interest in machine vision aided flexible manufacturing
cell (FMC) in the Egyptian market.
Implicit in the preceded figure is the fact that machine vision is a
multi-disciplinary subject and necessarily involves designers in
mechanical, optical, electrical, electronic (analogue and digital), and
software engineering and mathematical analysis (of image processing
procedures) [7].
Less obviously, several aspects of “software engineering” are also
required, including human–computer interfacing, work management, and
quality assurance procedures. Integrating such varied technologies to
create a harmonious, unified system is of paramount importance; failure to
do so properly will inevitably result in an unreliable and inefficient
machine.
We may observe that machine vision is a strong candidate in
Mechatronics applications since both terms dig into same areas [1].
1.2 Machine Vision and Computer Vision
Machine vision is not synonymous to Computer Vision (CV).
Computer vision is a branch of computer science where manipulation of
image is the primary concern. Computer vision is also known as digital
image processing where it involves image manipulation, analysis,
compression and presentation. On the other hand, machine vision is an
engineering discipline where software and hardware are tied to each other.
Several topics comprise machine vision systems such as
illumination, selection of cameras, interfacing circuitry and mechanical
actuation systems [1]. Less obviously, several aspects of “multi-domain
knowledge” are also required, including human–computer interfacing,
work management, and quality assurance procedures.
Hence, a team of engineers with a range of skills is needed to
design a successful Machine Vision system. Integrating such varied
technologies to create a harmonious, unified system is of paramount
importance; failure to do so properly will inevitably result in an unreliable
and inefficient machine.
4
This point cannot be over-emphasised. This is why we insisted in
the very first pages of this thesis that Machine Vision is quite distinct from
Computer Vision.
The point is made more forcibly in Table 1.1. The distinction
between machine vision and computer vision reflects the diversity that
exists between engineering and science. Entries in the central column
relate to the factory-floor Target Machine (TM), unless otherwise stated,
in which case they refer to an Interactive Prototyping Toolkit (IPT).
The following table (Table 1.1) depicts the major observations
between Machine Vision and Computer Vision.
Vision systems are currently being used extensively in
manufacturing industry, where they perform a very wide variety of
inspection, monitoring and control functions. Those areas of
manufacturing that have benefited most in the past include electronics,
automobiles, aircraft and domestic products from furniture polish and
tooth-paste to refrigerators and washing machines.
Vision systems have also been used in the food industry,
agriculture and horticulture, although to a smaller extent. Machine vision
is a part of many inspection processes nowadays, these may include:
Analysing the shape of whole products as a prelude to processing them
using robotic manipulators.
Analysing texture.
Counting.
Detecting foreign bodies.
Grading.
Measuring linear dimension
5
Table 1.1 The difference between machine vision and computer vision[8]
Feature Machine Vision Computer Vision
Motivation Practical Academic
Advanced in
theoretical sense
Unlikely. (Practical issues
are likely to dominate)
Yes. Many academic
papers contain a lot of
“deep” mathematics
Cost Critical Likely to be of
secondary importance
Dedicated
electronic
hardware
Possibly needed to
achieve high-speed
processing No (by definition)
Use integrated
solutions
Yes (e.g., systems are
likely to benefit from
careful lighting)
No. There is a strong
emphasis on proven
algorithmic methods
Data source A piece of metal, plastic,
glass, wood, etc. Computer file
Most important
criteria by which
a vision system is
judged
a. easy to use;
b. cost-effective;
c. consistent and reliable;
d. fast
Performance
Multi-disciplinary Yes No
Criterion for good
solution
Satisfactory
performance Optimal performance
Nature of subject Systems Engineering
(practical)
Computer Science,
academic (theoretical)
Human
interaction
IPT: vision engineer
TM: Low skill level
during set-up.
Autonomous in inspection
mode
Often relies on user
having specialist skills
(e.g., medical
background)
Operator skill
level required
a. IPT medium/high
b. TM must be able to
cope with low skill level
May rely on user
having specialist skills
(e.g., medical
background)
Output data Simple signal, to control
external equipment
Complex signal, for
human being
Principal factor
determining
processing speed
IPT: human interaction
TM: speed of production
Human interaction.
Often of secondary
importance
6
1.3 Machine Vision and Human Vision
Machine Vision does not set out to emulate human vision. At some
time in the future, it may be both possible and convenient to build a
machine that can “see” like a human being. At the moment, it is not. Today,
an industrial Machine Vision engineer is likely to regard any new
understanding that biologists or psychologists obtain about human or
animal vision as interesting but largely useless.
The reason is simple: the “computing machinery” used in the
natural world (networks of neurons) is quite different from that used by
electronic computers [9-11]. Certainly, no person can properly look
introspectively at his/her own thought processes in order to understand
how he/she analyses visual data. Moreover, there is no need to build
machines that see the world as we do. Even, if we could decide exactly
how a person sees the world, it is not necessary to build a machine to
perform that task in the same way.
In the natural world, there are clearly several different and
successful paradigms for visual processing including: insects, fish, and
mammals. In any case, such a machine would have the same weaknesses
and be susceptible to the same optical illusions as we do.
1.4 Inspection of ceramic plates
The ceramic tiles industrial sector is a relatively young industry
which has taken significant advantage of the strong evolution in the world
of automation in recent years. All production phases have been addressed
through various technical innovations, with the exception of the final stage
of the manufacturing process. This is concerned with visual surface
inspection in order to sort tiles into distinct categories or to reject those
found with defects and patterns faults. The generally accepted manual
method of tile inspection is labour intensive, slow and subjective.
7
Automated sorting and packing lines have been in existence for a
number of years, however, the complexity of inspecting tiles for damage
and selecting them against the individually set quality criteria of a
manufacturer has meant that, until recently, automated tile inspection has
not been possible.
1.5 Challenges in Automated Inspection
The research effort expended upon the problem of objectively
inspecting, analysing and characterizing ceramic tiles is easily justified by
the commercial and safety benefits to the industry:
Automation of a currently obsolete and subjective manual inspection
procedure
Significant reduction for the need of human presence in hazardous and
unhealthy environments
More robust and less costly inspection
Higher homogeneity within sorted classes of products
Tiredness and lack of concentration are common problems, leading
to errors in grading tiles. Gradual changes are difficult for human
inspectors to detect and it is possible that slight and progressive changes
will not be noticed at an early stage. Additionally, it is particularly difficult
for the human eye to accurately sort tiles into shades, with changing light
conditions in a factory [12]. Human decision is, as usual, influenced by
expectations and prior knowledge.
However this problem is not specific to structural defects. In many
detection tasks for example, edge detection, there is a gradual transition
from presence to absence. On the other hand, in “obvious” cases most
naïve observers agree that the defect is there, even when they cannot
identify the structure.
The goal of the inspection is not to give a statistical analysis of the
production but to classify every tile into quality-constant batches. These
tasks are often referred to as visual inspection.
8
In 1976, “H. Blossel” [13] developed the first successful model for
sorting ceramic tiles. The system inspects images captured by means of
pixel comparison of master plate and the plate under inspection. Although
the system was genuine in implementation, it lacks consideration of
mechanical handling system and interfacing with suggested controller
(Microprocessor). Moreover, it assumed that image is taken with no noise
which is hardly achieved up to this moment [2, 14, 15]. Meanwhile, to
inspect any plate, same pattern distribution must be retained to perform the
pixel comparison successfully.
“Desoli” developed system was concerned with corner defects in
ceramic tiles [16] by converting the captured image into binary image and
searching thereafter for edges by comparing each pixel to its surrounding
neighbours. He performed a comparison of edge detection filters and
showed that Sobel filter is the proper one to use in his application. The
system showed promising results (97.3 % correct classification). However,
the algorithm was affected by illumination that degrades edge detection
drastically [17]. Specification of practical implementation was not
mentioned too.
The research carried out in [17, 18] was divided into two parts; the
first part [18] proposed a group of inclined mirrors for indirect illumination
as a proposed approach to minimize reflectivity. They also proved that if
an image of the tile were taken from an oblique angle, cracks can be
detected easily. In The second part [17], Computer software was
implemented to perform online inspection. The program runs under DOS
and was written in C language. No information about the hardware used
was provided in this publication.
“Boukouvalas” added colour inspection capabilities to his system by
utilization of fuzzy logic to transfer the knowledge of domain expert into
the developed software [19]. The system was constrained by 6 different
colours to distinguish from. Colour matching yielded good results,
however, “Marques” [20] proposed a better rules by adding the change of
error during the inspection operation and therefore his system became
strong candidate to perform trend analysis. A year later, “Boukouvalas”
developed a system he named it “ASSIST” as an acronym of (Automatic
System for Surface Inspection and Sorting of Tiles) [21].
9
The system was performing inspection by utilization of 3 stages, a
Charge Coupled Device (CCD) colour camera for primary colour grading,
a monochrome camera for cracks detection and a line scan camera for
precision colour grading. The system has the ability to distribute its
computation over local area network connected to two servers. However,
the system failed to inspect plates in real time due to excessive
computation required by each process.
The oblique orientation of capturing camera were used again by
“Massen” and “Franz” [22]. They illustrated that although dimension
measurements is almost impossible from the camera perspective,
mounting a system of mirrors to have an indirect perpendicular view of the
ceramic plate would yield a better cracks detection and less distortion of
the view. They were arguing the possibility of such a system to produce
better results than visual inspection of humans. The developed system was
able to detect cracks based on a portion of the proposed system in [21]
(cracks detection).
They tackled the issue of intensive computation by estimating the
presence of cracks after performing a Fast Fourier Transform (FFT) of the
image and match it to a master plate to detect existence of cracks. The
system is limited to inspect cracks only without colour or shape
verification.
The developed technique by “Costa” and “Paretou” was based on
mounting 4 cameras at different angles [23]. A master template is captured
by each camera. Thereafter, a feature based technique is used to perform
image registration by means of linear transformation to calculate
translation, rotation and scaling. During the inspection stage, ceramic plate
is captured simultaneously by all cameras. The image is then compared to
a master template.
10
The technique is meant to combine views from different angles to
enhance image features that may not be an intuitive task to be obtained by
utilizing a single camera. The system detects cracks; however, it lacks the
capability to perform accurate measurements due to global nature of linear
transformation method. The system could be enhanced if an intensity
based method is used with correlation matrix to find similarity between
captured images. Authors pointed out that the limitation of the system is
the memory intensive calculations which are considered the bottleneck of
the system.
“Smith” and “Stamp” in [24] were motivated to investigate the 3D
surface of ceramic plates as conventional applications of vision-based
surface inspection. The system relied upon the analysis of abstract
projected features at the image plane for which, in general, is not possible
to reliably distinguish between three-dimensional topographic, and two-
dimensional chromatic features. Limitations in established techniques
generally follow as a consequence of what might be termed a conventional
‘viewer-cantered’ approach to object surface representation. They stated
whenever inspection takes place; the analysis of projected and abstracted
two-dimensional features within the image domain is restricted. Difficulty
arises as the appearance of these features will be closely dependent upon
the viewing and lighting configuration, and as a consequence these aspects
will normally be highly constrained.
The developed system is based on capturing a sequence of images
using a camera fixed in one position. Each image is captured with
controlled illumination synchronously which is also known as photometric
stereo approach [25] . 3D surface of ceramic plates is then constructed
from the intensity variation of captured image. An experimental work was
performed using a CCD camera with resolution 512 X 512 pixels and the
software was developed using C++ language. The system lacks accuracy
although it succeeded to draw the general shape of 3D ceramic surface.
11
By means of Self Organizing Maps (SOM) also known as Kohonen
feature map, “Kukkonen” and his colleagues built a system for colour
measurements for sorting five classes of brown tiles [26] . SOM utilizes
Euclidean distance for computing weights to weight vectors to neurons.
Meanwhile, the neuron with weight vector most similar to the input is
called the Best Matching Unit (BMU). The weights of the BMU and
neurons close to it in the SOM lattice are adjusted towards the input vector
which is the colour of each pixel and its position with respect to the top
left of the image. Thereafter, a K-Nearest Neighbour clustering is used for
fine tuning results. They compared results obtained from a trained 2D
(colour and position) SOM. Authors in [25] demonstrated that the
proposed system emulates human visual inspection to detect the preceded
tiles classes with a success rate of 97.6 %. However, they didn’t provide
any practical information regarding their experimental setup, which makes
comparing results to other methods impossible since their experiment
cannot be reproduced.
As a further development to the work published by “Kukkonen”, a
hybrid system of auto-associative and probabilistic neural network to
detect surface defects surface cracks on ceramic tiles [27] was carried out
by “Hocenski” and “Nyarko” . They explained their usage of the first
model to select the best features of the plate, and the latter one (the
probabilistic neural network) which is involved in classification.
They stated that utilization of Hopfield network minimized
computation since calculating weights is a one step process. Meanwhile,
probabilistic neural network which behaves like K-Nearest Neighbour
yielded better results compared to multilayer perceptron neural network.
Gaussian function was selected as the radial basis function for computing
the weight of each neighbour point. To perform an experimental work,
they used a CCD analogue camera with resolution of 400 X 240 pixels and
speed of 20 frames per second. Illumination technique was not discussed
which hardened the process of regenerating the experiment elsewhere.
12
The computation time and processing power were principal
motivations for “Elbehiery” et. al. to propose an algorithm based on the
principles of image processing and morphological operations on the
ceramic tile images[12] . The algorithm converts the captured coloured
image into grey scale to be able to perform intensity adjustment and
histogram equalization. Thereafter, thresholding the image to convert it to
binary format and applying edge detection algorithm by comparing each
pixel to its surrounding neighbours. Filling gaps between discontinued
lines is done as a final stage before searching for irregular shapes using
SDC morphological operation which are morphology operations
specialized for grey scale images that exists in MATLAB [28]. Eventually,
Inversing each pixel value in the binary image to enhance the visual
appearance of the crack detected is carried out.
Although the proposed system is quite easy to implement, however, it
requires a good deal of manual tuning. Furthermore, it totally ignored the
colour defects which are critical in ceramic industry[26]. Regardless the
latter point, the major advantage of such an algorithm is the ability to be
implemented as a low cost solution.
By the aid of wavelet analysis [29], “Elbehiery” et. al. proposed a
more robust technique than their previous work in [12]. They also
expanded their work to cover cracks and spots. The selection of wavelet
analysis as they illustrated in their paper enabled them to inspect the plate
regardless of variability in scale and resolution. Since calculating digital
wavelet transform yielded large amount of data, authors chose only a
subset of scales and positions. They employed TMPLAR, which stands for
Template Learning from Atomic Representations with different techniques
like Haar, Daubechies and Coiflets wavelet families.
They trained the system using only five iterations to reach
convergence. Thereafter, pattern classification was carried out using
measurements of variance between the template and ceramic plate under
inspection. The proposed algorithm was able to find cracks in textured
plates; however, it is observed that no experiments were performed on
ceramic plates with more than two different colours.
13
The published results showed only cracks without demonstrating the
detection of colour spots or any other geometric metrics.
“Novak” and “Hocenski” [15] investigated methods used to analyze
texture. They were categorized in two main categories: The first one,
called the statistical or stochastic approach, treats textures as statistical
phenomena. The formation of a texture is described with the statistical
properties of the intensities and positions of pixels. The second category,
called the structural approach, introduces the concept of texture primitives,
often called texels or textons.
To describe a texture, a vocabulary of texels and a description of their
relationships are needed. The goal is to describe complex structures with
simpler primitives, for example via graphs. They proposed the local binary
operator method for feature extraction since it is a monotonic process and
thus efficient under variable illumination methods.
The method uses two matrices, the first one is the weight of each
neighbour, and the second one represents the pixel of interest and its
surrounded neighbours. This method was originally used as a
complementary measure of local image contrast.
Since measuring the length of consecutive pixels and comparing it to
the master template, researcher proposed the method to be a part of
automated inspection system for ceramic tiles. The proposed algorithm
doesn’t cover colour inspection.
“Novak” extended his work in [30] by adding a pixel pair difference
method to search the image for surface defects as the method is mainly
used to encrypt the image. The method is simply commenced by flattening
the image into a vector holding the values of all pixels scanned in zigzag
manner. Thereafter, the difference in grey scale value of every 2 successive
no overlapping pixels were calculated. “Novak” used this method to get a
feature vector based on the encryption results by means of the difference
in histogram.
14
If the defected image has a different value other than expected , a
deeper investigation using the local binary operator he proposed in [15] is
to be performed consequently. The key advantage of such a method is
minimization of calculation power requirements.
The technique is much slower when implemented on colour images
by comparing all three colour planes Red, Green and Blue (RGB). The
system is constrained to cracks and doesn’t investigate geometry, angles
or colour spots.
“Ghita” et al [31] had successfully built a complete automated visual
inspection system for painted slates. The system employed edge detection
using median and Sobel edge detector, morphological analysis and
filtering objects by area to classify defects if exists. The major issue
confronted him was the surface reflectivity, which he and his colleagues
endeavoured to tackle using partial lighting for illumination while using
oblique angle for camera acquisition. The system was able to yield
promising results. However, in regard to accurate geometry metrics,
acquiring images from oblique angle may not provide satisfactory results.
Moreover, evaluation of defects was the primary concern while colour
homogeneity and drawing geometry were ignored. The system was
published in details which is a major advantage for researchers carrying
out further development.
In 2006, “Vasilic” et. al. conducted a survey for edge detection
methods used to inspect ceramic plates [31] . The study was investigating
Prewitt, Sobel, Median and Canny edge detectors. The study was
performed over 23 different types of ceramic plates and researchers
concluded that Sobel filter sustained variability in shape and illumination
and thus recommended in this type of applications.
This conclusion comes in agreement with later publication by
“Vincent” and “Folorunso” [32] as they affirmed that Sobel filter
improves the quality of images taken under extremely unfavourable
conditions in several ways: brightness and contrast adjustment, edge
detection, noise reduction, focus adjustment, motion blur reduction.
15
Meanwhile, the Sobel operator is based on convolving the image with
a small, separable, and integer valued filter in horizontal and vertical
direction and is therefore relatively inexpensive in terms of computation
power.
1.6 Statement of the problem
The present problem can be summarized in the following points:
Most of the published work related to ceramic inspection is focusing
on finding defects without considering practical aspects of
implementation such as camera types, lens distortion, illumination,
electrical circuitry and mechanical actuation system.
Since garnished ceramic plates industry is a fast growing field, new
inspection demands became mandatory. This involves wider
inspection procedure covering drawing geometry, angles, orientation
and colour of each drawn object.
Practical implementation may be discussed in more details to allow
future work to be carried out in an easier way.
1.7 Objective of the present work
Since garnished wall plates require more inspection procedures
than conventional peer covered in the preceded survey, present work is
aimed to:
Develop an algorithm to inspect geometry, angles, orientation and
colour of each drawn object found in the garnished plate. The
developed system is required to maintain the inspection process
performed without stopping the production line.
Design and implement a mechatronic system as a Flexible
Manufacturing Cell (FMC) to implement the proposed algorithm.
The system will comprise a software program to perform the
inspection and control the FMC, a mechanical actuation system for
sorting the ceramic plates and an electronic circuit which will serve
as an interfacing layer between software and mechanical sorting
system.
16
Studying the impact of developed system on productivity.
The suggested algorithm may be structurally distilled into five pillars:
I. Image acquisition and pre-processing: for removing noise
and correcting the lens distortion by means of proposed
calibration algorithm.
II. Soft partitioning and colour grouping: for creating boundary
region among each drawn object in the ceramic plate based on
the degree of membership of all its inbound pixels to expected
colour patterns.
III. Geometric feature extraction: Whereby, dimensions of
drawn objects are extracted via edge detection and fitting the
points detected by means of an array of spokes.
IV. Classification using fuzzy logic: The final stage which yields
the ceramic grade (acceptable, colour spot or colour mismatch)
based on proposed fuzzy inference system.
Figure 1.3 Proposed inspection algorithm for garnished ceramic wall plates
Image acquisition and pre-processing
(Chapter 1)
Soft partitioning and colour grouping
(Chapter 2)
Geometric feature extraction
(Chapter 3)
Fuzzy classification
(Chapter 4)
17
Image Acquisition and Pre-processing
This chapter depicts the first step in the proposed algorithm aiming
to prepare the image for processing stage. A proposed algorithm for image
calibration is discussed. Thereafter, the experimental setup for image
acquisition, gamma correction and colour adjustment is shown in details.
2.1 Spatial Calibration
Spatial calibration refers to the process of correlating the pixels of
an acquired image to real features in the image. This process can be used
to make accurate measurements in real-world units like millimetres instead
of pixels, and to correct for camera perspective and lens distortion [33]. As
illustrated in Figure 2.1, a grid of equally distant dots is placed in the
camera field of view. Thereafter, an image is taken for the calibration grid
and compared to the expected distribution of dots in order to perform
image correction.
Spatial calibration produces a mapping to depict how each pixel
relates to a real-world location. The image data itself is unaffected by this
process. Image correction actually applies the learned calibration
information to remove camera perspective or lens distortion effects from
an image. This process is computationally intensive. So in many cases it is
preferred to store the calibration results with the image and apply it as
necessary.
Figure 2.1 Notion of image calibration
18
a) Barrel distortion b) Pincushion distortion
2.1.1 Lens distortion
Distortion is one of the worst problems limiting measurement
accuracy; even the best performing optics are affected by some grade of
distortion, while often even a single pixel of difference between the real
image and the expected image could be critical. Distortion is simply
defined as the percentage difference between the distance of an image
point from the image centre and the same distance as it would be measured
in a distortion-free image; it can be thought of as a deviation between the
imaged object and the real dimensions of that object.
Figure 2.2 Types of lens distortion
Although distortion can be irregular or follow many patterns, the
most commonly encountered distortions are radially symmetric, or
approximately so, arising from the symmetry of a photographic lens. The
radial distortion can usually be classified as one of two main types. In
"barrel distortion", image magnification decreases with distance from the
optical axis. The apparent effect is that of an image which has been mapped
around a sphere (or barrel) as shown in Figure 2.2a.
While In "pincushion distortion" (Figure 2.2b), image
magnification increases with the distance from the optical axis. The visible
effect is that lines that do not go through the centre of the image are bowed
inwards, towards the centre of the image, like a pincushion. To correct
such distortion, two approaches are used.
2.1.2 Telecentric Lens
Common lenses give different magnifications at different
conjugates: as such, when the object is displaced, the size of its image
changes almost proportionally with the object-to-lens distance. This is
something anybody can easily experience in everyday life, for example
when taking pictures with a camera equipped with a standard photographic
lens [5].
19
With telecentric lenses, the image size is left unchanged with object
displacement, provided that the object stays within a certain range often
referred to as Depth of Field or Telecentric Range. This is due to the
particular path of the rays within the optical system: only ray cones whose
principal ray is parallel to the opto-mechanical main axis are collected by
the objective. For this reason, the front lens diameter must be at least as
large as the object field diagonal.
This optical behaviour is obtained by positioning the stop aperture
exactly on the focal plane of the front optical group (Figure 2.3): the
incoming rays aim at the entrance pupil which appears as being virtually
placed at the infinity. The name telecentric is derived from the two
syllables “tele” which means “far” in ancient Greek and “centre” which
accounts for the pupil aperture, the actual centre of an optical system [34].
Figure 2.3 In a telecentric system rays get into the optics only
The teleentric lens yields a high level correction of lens distortion;
however, the cost of such a lens is extremely expensive to be used in
prototyping in moderately funded projects. Therefore, software based lens
error correction is considered a much economic approach.
2.1.3 Software based correction
Correcting lens distortion by means of computer software has been
challenging problem in the last decade. The key advantage of utilization
of software is the reduction of cost compared to usage of hardware
telecentric lens. The challenge emerges from the complicated image
processing techniques combined to reduce such an error which will be
discussed in the next sections.
20
2.2 Proposed algorithm for correcting and calibrating lens
distortion
Software aided lens distortion correction and calibration has been
developed to overcome the problem of distorted barrel shaped ceramic
plate image by means of image processing.
Figure 2.4 The proposed algorithm for perspective correction
The proposed algorithm offers a low cost solution by implementing
software manipulation of the image as shown in Figure 2.4. The key
objective is to estimate the original position of each pixel which corrects
the grid spacing and dots radii. The grid used has circles with 2mm radii
and 5mm distant from each other.
2.2.1 Colour plane extraction
Every digital coloured image is composed of rows and columns of
pixels. Each pixel colour is formed by a combination of red, green and blue
components called planes (8-bits for each colour plane). Depending on the
value stored in each colour plane, the pixel gains its visual colour. The
screenshot annotated by Figure 2.5 shows the resulted image after
extracting the red component of each pixel which produced the shown grey
scale image where pixels shown are resampled down from the original
coloured image (24 bit depth) to have 256 shades due to its 8 bits red colour
portion. List 2.1 illustrates the pseudo code for extracting the red
component from each pixel in the calibration grid. The red plane
experimentally proved to yield higher contrast and hence better correction.
Image
Acquisition
Colour Plane
Extraction
Automatic
Thresholding Particle
Grouping
Particle
Measurements
Displaying
corrected image
Distance
Measurements
Mapping particles
to its expected place
21
Figure 2.5 Screenshot of calibration grid used in correction after red plane extraction
List 2.1 Colour plane extraction algorithm
2.2.2 Automatic Thresholding
Thresholding an image is meant to reduce the pixel representation
from 8 bits which is known as grey image to a single bit or binary image.
This method can be achieved using several techniques. The chosen method
for performing automatic thresholding was by means of clustering
algorithm.
Clustering is considered one of the effective adaptive algorithms in
binary thresholding. It sorts the histogram of the image within a discrete
number of classes corresponding to the number of phases perceived in an
image [35].
1. Start at first row and first column in the image.
2. Get the red component of the current pixel located in
current row and column
3. Save the new pixel value in same index in grey
image format.
4. Increment row number
5. If row count reaches the last row of the image,
increment column count by one. Else go to step2
6. If column count reaches the last column of the
image, stop. Else, go to step 2
22
The grey values are determined, and a barycentre is determined for
each class. This process is repeated until it reaches a value that represents
the centre of mass for each phase or class is obtained.
Let p(x,y) be the pixel value of pixel located at xth row and yth
column. The threshold value is the pixel value k for which the following
condition is true:
𝑘 =𝜇1+𝜇2
2 2.1
Where μ1 is the mean of all pixel values that lie between 0 and k,
and μ2 is the mean of all pixel values that lie between k + 1 and 255. In
other words:
𝜇1 =∑ 𝑝(𝑥,𝑦)𝑘
0
𝑁𝑜.𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 ℎ𝑎𝑣𝑖𝑛𝑔 𝑣𝑎𝑙𝑢𝑒 ≤𝑘 ; 0 ≤ 𝑝(𝑥, 𝑦) ≤ 𝑘 2.2
and 𝜇2 =∑ 𝑝(𝑥,𝑦)255
𝑘+1
𝑁𝑜.𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 ℎ𝑎𝑣𝑖𝑛𝑔 𝑣𝑎𝑙𝑢𝑒 ≥(𝑘+1) ; 𝑘 + 1 ≤ 𝑝(𝑥, 𝑦) ≤ 255 2.3
Figure 2.6 Adaptive thresholding using clustering technique
After this stage (Figure 2.6), the grid is divided into two classes,
the white pixels representing dots and the black pixels forming the
background and hence separating the grid dots is achieved as implemented
using the algorithm depicted in List 2.2.
23
List 2.2 Pseudo of thresholding using clustering algorithm
2.2.3 Particle grouping
After thresholding the image, it is necessary to find all grids (dots)
appeared past thresholding. To do so, it is required to group neighboured
particles that forming a complete dot (circle).
The easiest way is to track boundaries of each particle and track its
neighbours until returning to the starting point (aka seeding point). If
tracking reached an end point which differs from the starting one, it means
incomplete circle and it is excluded later from calibration calculation.
Figure 2.7 Resulted image of boundary tracking of grid dots
Each Circle is stored in an array to be treated independently. To
correct the geometry and location of each dot, few measurements are
needed. It is observable that incomplete circles exist around the grid border
since the field of view of the camera doesn’t cover all the whole grid area.
1. Initialize k=1
2. Calculate 𝜇1 𝑎𝑛𝑑 𝜇2
3. If 𝑘 =𝜇1+𝜇2
2 then replace each pixel value less than
k with zero, and one to every pixel with grey value
equal or above k.
4. If step 3 is false, increment the value of k and go to
step 3 again.
24
Figure 2.7 shows the detected boundaries of each grid while the algorithm for achieving
such a task is illustrated in
List 2.3 by means of finding seeding point of each boundary and
follow the boundary until reaching the seeding point again.
List 2.3 Boundary extraction of grids contour
2.2.4 Particles and distance measurements
After boundaries are found, two measurements are carried out to
correct the image:
Area and perimeter of each dot: to correct distorted dots, area
is measured by counting number of pixels inside each boundary
region. Meanwhile, perimeter is calculated as the number of
pixels of the boundary.
Compactness factor: This is known as the ratio of the
perimeter squared over area. The circle has a minimum
compactness factor (4π). The compactness of each particle
determines if skewness takes place in order to be corrected.
Distance between centres of adjacent particles: There are
several methods for measuring distance between pixels, the
most known one is Euclidean which is defined as:
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑖𝑛 𝑝𝑖𝑥𝑒𝑙𝑠 = √(𝑥2 − 𝑥1)2 − (𝑦2 − 𝑦1)2 2.4
Where x1, y1 and x2, y2 are coordinates of two adjacent centres
respectively. As shown in Figure 2.8, incomplete circles have odd centres
and thus were excluded from correction process (Figure 2.9).
1. Start at first row and first column in image.
2. Search for first non-zero pixel and label it S.
3. Search for the 8 neighbored pixels till finding
another pixel.
4. Make the last found pixel C and start searching for
non-zero pixels in its neighbors.
5. If all 8 neighbors are black, store it in N array.
6. Repeat steps 3 and 4 until returning to pixel S.
7. Increment search after pixel S
8. Go to step 2 until image is totally searched
25
Figure 2.8 Detecting centres to check for skewness of each dot
Centres can be determined by measuring distance between two
horizontal pixels on boundary of any object and finding the intersecting
point with any other 2 pixels on the same boundary.
These centres serve to correct the location of each dot with respect
to its neighboured peers. Meanwhile, the centre location accompanied by
compactness factor of each dot will be employed to correct the pixel
location of each pixel in this dot.
Figure 2.9 Visualization of detected dots in the grid
Barrel distortion
26
2.2.5 Performing correction on each pixel
By evaluating the area, perimeter and skewness, it is possible to
correct the distorted image by restoring each circle area and position. This
process is not difficult since previous knowledge about the original grid
paper is known in terms of radii and location of each dot.
Figure 2.10 Pixel mapping to restore expected grids locations
List 2.4 Restoration of grids circular shape
List 2.5 Retrieval of grids corrected locations
1. Locate first dot in the image.
2. Check for compactness.
3. Move pixels till compactness factor is minimized.
4. Copy pixels around boundary till reaching the
expected area.
5. Store new pixel locations and create the new pixels
correction lookup table.
6. Repeat for each object until finishing all objects in
the image.
1. Locate first object center in the top left of the image.
2. Measure distance between nearest horizontal and
vertical objects
3. Move objects and its surrounding objects to the
expected distance.
4. Locate the next object.
5. Repeat step 2 to 4 until all objects are processed.
6. Store new pixels locations
27
The procedure simply rearranges pixels to restore the expected grid
distribution as shown in Figure 2.10. In List 2.4, the algorithm for
correcting the distorted shape of every grid is stated. Meanwhile,
relocating the corrected grids to its original location is detailed in List 2.5.
After this stage, any acquired image is mapped to the new
correction map (aka lookup table) and thus measuring errors is minimized.
As a result, Figure 2.11 forms the acquired image which appears distorted
as the acquired gird. Such a distortion causes drastic skewness which is
observed in the drawn objects near the image border.
Figure 2.11 Acquired image of ceramic plate
Thereafter, the correction pixel map extracted from the calibration
stage is applied to correct each pixel location as shown in Figure 2.12. The
minimization of skewness could be seen in the corrected image.
Observable
skewness
28
Figure 2.12 The Corrected image after applying the algorithm
Figure 2.13 Screenshot of the developed software for calibration of ceramic plate
Corrected
image
29
Since the calibration process performs pixel relocation, when
moving dots near the border few places with empty dots appear (black
area) of about 1 cm wide and have a side length of the ceramic plate. This
area is excluded from the inspection stage.
A screenshot shown (Figure 2.13) depicts the developed software
carried out where image is loaded offline, thereafter, the proposed
algorithm is executed and an overall average execution time is displayed
to measure the execution speed of the proposed algorithm.
2.3 Image acquisition
The first stage of any vision system is the image acquisition stage.
After the image has been obtained, various methods of processing can be
applied to the image to perform many different vision tasks required today
[6]. However, if the image has not been acquired satisfactorily then the
intended tasks may not be achievable, even with the aid of some form of
image enhancement. The conducted survey covered the commonly used
cameras in machine vision systems:
2.3.1 Analogue cameras
Analogue cameras are cameras that generate a video signal in
analogue format. The analogue signal is digitized by an image acquisition
device. The video signal is based on the television standard, making
analogue the most common standard for representing video signals.
A charge-coupled device (CCD) is an array of hundreds of
thousands of interconnected semiconductors. Each pixel is a solid-state,
photosensitive element that generates and stores an electric charge when it
is illuminated. The pixel is the building block for the CCD imager, a
rectangular array of pixels on which an image of the scene is focused. In
most configurations, the sensor includes the circuitry that stores and
transfers its charge to a shift register, which converts the spatial array of
charges in the CCD imager into a time-varying video signal. Timing
information for the vertical and horizontal positions and the sensor value
combine to form the video signal [36] .
30
Figure 2.14 CCD array configuration
For standard analogue cameras, the lines of the CCD are interlaced
to increase the perceived image update rate. This means that the odd-
numbered rows (the odd field) are scanned first. Then the even-numbered
fields (the even field) are scanned.
2.3.2 Digital Cameras
Digital cameras have several advantages over analogue cameras.
Analogue video is more susceptible to noise during transmission than
digital video. By digitizing at the camera level rather than at the image
acquisition device, the signal-to-noise ratio is typically higher, resulting in
better accuracy. Because digital cameras are not required to conform to
television standards, they can offer larger image sizes and faster frame
rates, as well as higher pixel resolutions. Digital cameras come with 10 to
16-bit gray levels of resolution as a standard for machine vision,
astronomy, microscopy, and thermal imaging applications. Digital
cameras use the same CCD type devices for acquiring images as analogue;
they simply digitize the video before sending it to the frame grabber.
Sensors Vertical Shift Register
Output
Horizontal Shift Register
31
2.3.2.1 Camera Link
Camera Link is an interface specification for cables that connect
digital cameras to image acquisition devices. It preserves the benefits of
digital cameras – such as flexibility for many types of sensors, yet it has
only a small connector and one or two identical cables, which work with
all Camera Link image acquisition devices.
Camera Link greatly simplifies cabling, which can be a complex
task when working with standard digital cameras. The constraint of using
such a type is the expensive cost which may not suit prototyping stage in
proposed research.
2.3.2.2 USB cameras
USB cameras have a major advantage over all other types,
portability. In other words, it requires no special hardware other than
dedicated USB port. Although USB cameras offered in market supports
high resolution (up to 6 MP), the acquisition speed is poor.
2.3.2.3 IEEE 1394 (Firewire cameras)
IEEE 1394 is a serial bus standard used by many PC peripherals,
including digital cameras. IEEE 1394 cameras use a simple, flexible, 4 or
6-wire power cable; and in some cases, the bus can supply power to the
camera. However, because IEEE 1394 is a shared bus, there is a bandwidth
limitation of approximately 250 MB/s when no other device is connected
to the bus.
IEEE 1394 cameras also require processor control to move the
image data, which limits available processor bandwidth for image
processing. IEEE 1394 is a standard that also includes functions for
enumerating and setting up the camera capabilities.
A firewire camera with 640 X 480 pixels was selected in the
present work for the following reasons:
It has a high frame rate per second (60 fps) which is sufficient to
acquire images online without the need to stop the conveyor
carrying ceramic plates under inspection.
32
The cost is relatively moderate compared to other high speed
acquisition peers. For instance, it costs nearly 30% of the price of
the camera link camera.
Table 2.1 Comparison of available cameras utilized in machine vision application
Analogue
Cameras
Parallel
Digital
Cameras
Camera
Link
Cameras
IEEE 1394
Cameras
Data Rate Up to 4
MB/S
Up to 120
MB/S
Up to 510
MB/S
Up to 250
MB/S
Spatial
Resolution Low High High Medium
Functionality Simple and
easy Advanced Advanced
Simple and
easy
Pixel Depth (per colour plane)
8-bit to 10-
bit
Up to 16-
bit
Up to 16-
bit
Typically
8-bit
Cabling
Simple
BNC
cabling
Thicker,
custom
cabling
Simple,
standard
cabling
Simple,
standard
cabling
2.4 Acquisition parameters
The image acquisition has several attributes to be set .These
attributes are affected by external parameters like the surrounding
illumination and object reflectivity. Firewire cameras is accompanied with
software Application Programming Interfacing functions (APIs) to allow
software programming languages such as Visual Basic and MATLAB to
adjust such parameters. When performing parameter adjustment, the rule
of thumb is to use colourful objects for obtaining satisfactory results. The
following subsections demonstrate the key attributes and experimental
setup of the developed flexible cell for acquisition from the chosen firewire
camera.
2.4.1 Image format
The utilized firewire camera has several built in formats. Each
format comprises pre-processing functions such as the image size in pixels
and the colour depth as shown in Figure 2.15. The setup was commenced
using 656 X 490 and then optimized to 640 X 480 pixels.
33
2.4.2 Speed
The speed is the representation of the data rate mentioned earlier
in the comparison table. The speed affects the maximum image size and
the delivered frames per second. The camera has a speed of 100 mb/s.
Figure 2.15 Image format and delivered speed rate
2.4.3 Bayer colour filter
A Bayer filter is a Colour Filter Array (CFA) for arranging RGB
colour filters on a square grid of photo-sensors. Its particular arrangement
of colour filters is used in most single-chip digital image sensors used in
digital cameras, camcorders, and scanners to create a colour image.
34
The filter pattern is 50% green, 25% red and 25% blue, hence is
also called GRGB or other permutation such as RGGB as in Figure 2.16a.
a) The Bayer arrangement of colour filters on the pixel array of an image sensor
b) Configuring Bayer pattern and RGB colour gain of the camera mounted on the test
rig
Figure 2.16 Bayer filter (concept and configuration)
35
This behaviour mimics the physiology of the human eye. The retina
has more rod cells than cone cells and rod cells are most sensitive to green
light. These elements are referred to as sensor elements, sensels, pixel
sensors, or simply pixels; sample values sensed by them, after
interpolation, become image pixels.
2.4.4 Brightness and contrast
Brightness and contrast represent a way to adjust an image. They
come from the display technology, being common controls in all monitors.
The colour brightness/contrast is similar to the grey scale counterparts, in
most cases being applied to all RGB channels. For a grey scale image,
brightness represents an image adjustment where a constant value is added
to all pixel values. The contrast adjustment is a multiplication of the pixel
values with a constant. The value was using trials and error as depicted in
Figure 2.17.
Figure 2.17 Brightness adjustment
36
2.4.5 Gain
Since the camera used in the conducted research activity has an on-
board 10 bits A/D converter, the camera offers gain control where mapping
the raw 10 bits of each colour plane can be reduced to 8 bits. The gain
controls which A/D range (0-1024) is mapped to the 8 bits counterparts
(Figure 2.18).
Figure 2.18 Gain control
A glossy cover was chosen with different colours to serve two
objectives:
To check the colour quality of the acquired image.
To adjust the illumination level to minimize reflectivity without
affecting the colour adjustment.
37
a- Response of gamma correction b- Pseudo code of gamma correction
2.5 Gamma Correction
Gamma correction, gamma nonlinearity, gamma encoding, or
often simply gamma, is the name of a nonlinear operation used to code and
decode luminance values in video or still image systems. Gamma
correction is defined by the following power-law expression:
𝑉𝑜𝑢𝑡 = 𝑉𝑖𝑛𝛾
2.5
Where the input voltage from the acquisition camera (Vin) and output
values displayed on the screen (Vout) are non-negative real values, typically
in a predetermined range such as 0 to 1[36]. To implement gamma
correction digitally as shown in Figure 2.20, each pixel value in every
colour plane (Red, Green and Blue) is raised to correction factor (γ) using
the following formula:
𝑃𝑜𝑢𝑡 = 255 × (𝑃𝑖𝑛
255)𝛾
2.6
The range of values used for gamma will depend on the application.
The selected value for correcting the acquired ceramic image was found to
be 0.7 based on trial and error in the three colour planes of the image.
Figure 2.19 Gamma correction of the acquired ceramic image (γ=0.7)
For i=0 to Max. Row Count
For j=0 to Max. Row Count
GetPixelColour(i,j)
newRed = 255 * (Red(colour) / 255) ^ gammaCorrection
newGreen = 255 * (Green(colour) / 255) ^
gammaCorrection
newBlue = 255 * (Blue(colour) / 255) ^ gammaCorrection
Next j
Nect i
PutPixelColour(x, y) = RGB(newRed, newGreen, newBlue)
38
Figure 2.20 Implementation of Gamma Correction
As shown in Figure 2.20, the gamma correction drastically
enhanced the acquired image which appeared to be washed out due to
gamma effect.
a-Raw image b-Gamma correction
39
Soft Partitioning and Colour Grouping
In this chapter, the method of fuzzy c-mean is introduced. The
proposed algorithm for colour sorting and grouping is also discussed in
details.
3.1 Clustering
Cluster Analysis encompasses a number of different algorithms and
methods for grouping objects of similar type into respective categories. A
general question facing researchers in many areas of inquiry is how to
organize observed data into meaningful structures, that is, to develop
taxonomies or classes[37]. In other words cluster analysis is an exploratory
data analysis tool which aims at sorting different objects into groups in a
way that the degree of association between two objects is maximal if they
belong to the same group and minimal otherwise as shown in Figure 3.1.
Cluster analysis can be used to discover structures in data without
providing an explanation or interpretation. In other words, cluster analysis
simply discovers structures in data without explaining why they exist.
Figure 3.1 Clustering is performed by grouping each similar data based on common
criteria
There are a countless number of examples in where clustering plays
an important role. For instance, biologists have to organize the different
species of animals before a meaningful description of the differences
between animals is possible.
Dataset Clustering
40
According to the modern system employed in biology, man
belongs to the primates, the mammals, the amniotes, the vertebrates, and
the animals. The higher the level of aggregation the less similar are the
members in the respective class.
Clustering is one of the effective methods for classifications
nowadays. It has been employed in coal classification used in thermal
power stations[38], for Electroencephalogram signal classification[39],
even as an efficient algorithm for estimation of insects invasion[40] and
for bearing fault detection[41].
3.2 Problem of clustering
Let’s consider a dataset 𝑋 consisting of data points 𝑥𝑖 (1 ≤ 𝑖 ≤𝑁)representing objects, patterns, pixels….etc and 𝑥𝑖 = {𝑎𝑖1, 𝑎𝑖1, … . . 𝑎𝑖𝑑} where each element in 𝑥𝑖 denotes a nominal attribute (colour, surface
roughness, shape…. etc.
The problem is to find adequate criteria called nodes to group data
points around it to evaluate the overall classification of each data point.
3.3 Advantages of using clustering
Clustering can be beneficial solution whenever scalability is
required. The algorithm is also capable of dealing with different types of
data which in conclusion offers high dimensionality [32, 37].
3.4 Constraints of using clustering
One of the major restrictions in using clustering techniques is a
time consuming process when dealing with large set of data. This
behaviour can be prohibited in time critical applications like web search
engines. Meanwhile, the effectiveness of classification depends on the
proper definition of similarity [42].
41
3.5 Methodologies of distance measurements
An important step in most clustering is to select a distance measure,
which will determine how the similarity of two elements is calculated. This
will influence the shape of the clusters, as some elements may be close to
one another according to one distance and farther away according to
another.
3.5.1 Common distance functions
The following review covers the most commonly used distance
measures techniques for distance measure assisted clustering:
3.5.1.1 The Euclidean distance
It is also called 2-norm distance. A review of cluster analysis in
pattern classification research found that the most common distance
measure in published studies in that research area is the Euclidean distance
or the squared Euclidean distance [43].
The Euclidean distance between point p and point q is the length
of the line segment 𝑝𝑞̅̅ ̅. In Cartesian coordinates, if 𝑝 = (𝑝1, 𝑝2, . . . , 𝑝𝑛)
and 𝑞 = (𝑞1, 𝑞2, . . . , 𝑞𝑛) are two points in Euclidean n-space, then the
distance from p to q is given by:
𝑑(𝑝, 𝑞) = √∑ (𝑝𝑖 − 𝑞𝑖)2𝑛𝑖=1 3.1
According to this, in a 2-dimensional space, the distance between
the point (x = 1, y = 0) and the origin (x = 0, y = 0) is always 1
according to the usual norms, but the distance between the point (x =
1, y = 1) and the origin will be √2.
3.5.1.2 The Manhattan distance (aka taxicab norm or 1-norm)
This method was derived by “Hermann Minkowski” in the 19th
century. In comparison to Euclidean geometry, the usual distance function
or metric is replaced by a new metric in which the distance between two
points is the sum of the absolute differences of their coordinates.
42
Figure 3.2 Taxicab geometry vs. Euclidean distance
In other words:
𝑑(𝑝, 𝑞) = ∑ |𝑝𝑖 − 𝑞𝑖|𝑛𝑖=1 3.2
Recalculating the example illustrated in the previous section
(section 3.5.1.1), the distance measured using Manhattan technique will be
2. As shown in Figure 3.2, the red, blue, and yellow lines have the same
length (𝟏𝟐) in taxicab geometry for the same route. In Euclidean
geometry, the green line has length of 𝟔 × √𝟐 ≈ 𝟖. 𝟒𝟖, and is the unique
shortest path.
3.5.1.3 The Hamming distance
Figure 3.3 Example of hamming distance
43
This method measures the minimum number of substitutions
required to change one member into another. For instance, the hamming
distance between 2173896 and 2233796 is 3. In Figure 3.3, two examples
are given to calculate the hamming distance from 0110 to 1110 which is 1
(blue path) and from 0100 to 1001 which is 3 (red path).
3.6 Clustering Techniques
Hierarchical algorithms find successive clusters using previously
established clusters. These algorithms usually are either agglomerative
("bottom-up") or divisive ("top-down"). Agglomerative algorithms begin
with each element as a separate cluster and merge them into successively
larger clusters. Divisive algorithms begin with the whole set and proceed
to divide it into successively smaller clusters.
Partitioning algorithms typically determine all clusters at once, but
can also be used as divisive algorithms in the hierarchical clustering.
Density-based clustering algorithms are devised to discover
arbitrary-shaped clusters. In this approach, a cluster is regarded as a region
in which the density of data objects exceeds a threshold.
Subspace clustering methods look for clusters that can only be seen
in a particular projection (subspace, manifold) of the data. These methods
thus can ignore irrelevant attributes. The general problem is also known as
correlation clustering while the special case of axis-parallel subspaces is
also known as two-way clustering, co-clustering or bi-clustering.
In these methods not only the objects are clustered but also the
features of the objects, i.e., if the data is represented in a data matrix, the
rows and columns are clustered simultaneously. They usually do not
however work with arbitrary feature combinations as in general subspace
methods. But this special case deserves attention due to its applications in
bioinformatics.
Many clustering algorithms require the specification of the number
of clusters to produce in the input data set, prior to execution of the
algorithm. Barring knowledge of the proper value beforehand, the
appropriate value must be determined, a problem on its own for which a
number of techniques have been developed.
44
3.7 Effectiveness of colour clustering
The objective of colour clustering is to divide a colour set into
homogeneous colour clusters. Colour clustering is used in a variety of
applications, such as colour image segmentation and recognition. Fuzzy
clustering models have proved a particularly promising solution to the
colour clustering problem [44]. Such unsupervised models can be used
with any number of features and clusters.
In addition, they distribute membership values across the clusters
based on natural groupings in feature space of the fuzzy clustering
algorithms proposed to date. The fuzzy c-means (FCM) algorithm is the
most widely used [45].
3.7.1 The need for fuzzification in colour grouping
Colour clustering is an inherently ambiguous task because colour
boundaries are often blurred. For example, consider the task of dividing a
colour image into colour objects. In colour images, the boundaries between
objects are blurred and distorted due to the imaging acquisition process.
Furthermore, object definitions are not always crisp, and knowledge about
the objects in a scene may be vague [46].
Fuzzy set theory and fuzzy logic are ideally suited to deal with such
uncertainties. In fuzzy clustering, the uncertainty inherent in a system is
preserved as long as possible before decisions are made. As a result of this
approach, fuzzy clustering is less prone to falling into local optima than
most of other crisp clustering algorithms [47].
3.7.2 Challenges in fuzzy clustering
There are three major difficulties in fuzzy clustering:
Determining the optimal number of clusters to be created (most
algorithms require the user to specify the number of clusters).
Choosing the initial cluster centroids (most algorithms choose a
random selection because such a selection affects the duration of
the iterative process).
Handling data characterized by large variability in cluster shape,
cluster density, and the number of points in different clusters.
45
3.7.3 Proposed soft partitioning and group visualization using fuzzy
clustering (fuzzy c-mean)
Colour grouping is meant to isolate objects with similar colours to
be prepared for feature extraction. Since pixels of same object may have
variability in colours, thus partitioning could be achieved by iterative fuzzy
c-mean where each centre depicts the RGB colour components of painted
object. This information is taken manually by human operator.
Finding all regions that share the same colour with degree of
membership is carried out by finding the local minimum of the objective
function. To inspect the quality of garnished wall plates, the colour of each
drawn object is utilised for segmentation using fuzzy C-Mean (aka fuzzy
k-Mean). Fuzzy C-mean was developed by J.C. Bezdek[48].
Let P=(𝐶1, 𝐶2, 𝐶3 …𝐶𝑖) be the colour clusters observed in the image
where 𝑖 ≠ 0, and 𝜇𝑐𝑖be the degree of membership of each pixel RGB value
𝑥𝑖 = (𝑥𝑖𝑟 ,𝑥𝑖𝑔 , 𝑥𝑖𝑏) or colour feature vector) that belong to cluster 𝐶𝑖
where 0 ≤ 𝜇𝑐𝑖≤ 1. X is the total number of pixels in the image.
Finding all regions that share the same colour with degree of
membership is carried out by finding the local minimum of the objective
function. This is implemented by applying the formulas below[1]:
μci(x) =
1
∑ (‖x−vi‖
2
‖x−vj‖2)k
j=1
, 1 ≤ i ≤ k, x ∈ X Eq. 3.3
𝒗𝒊 =∑ (𝜇𝑐𝑖
(𝑥))𝑚
× 𝑥𝑥∈𝑋
∑ (𝜇𝑐𝑖(𝑥))
𝑚
𝑥∈𝑋
, 1 ≤ 𝑖 ≤ 𝑘 Eq. 3.4
𝒗𝒊𝒕+𝟏 − 𝒗𝒊
𝒕 ≤ 𝜀 Eq. 3.5
Where 𝑣𝑖 is the prototype of ith cluster that minimizes the objective
function, m was chosen to be 2 and 𝜖 is the acceptable error before stopping
the iteration process. The symbol t refers to the previous iteration. Each
cluster represents distinctive colour in the ceramic plate.
46
Figure 3.4 visual representation of clustering result on the garnished wall plates
After this step, all pixels in the template image are mapped to its
appropriate cluster, isolated pixels among large group of pixels belong to
different clusters are treated as part of the surrounding object features.
Figure 3.4 illustrates the impact of clustering in terms of separating
features (hollow rectangles) from the background (flat surface). Colour
nodes RGB values were set using the average vote of the blue, red and
mild white colour rating.
List 3.1 Colour sorting with fuzzy c-mean
1. Load master ceramic image template.
2. Select each distinctive drawn object RGB components by
operator.
3. Store each distinctive colour in the master image in a
structure=( C1, C2…. Cn) comprising 3 bytes X1, X2 and
X3(for red, green and blue components) where Cn
represents the nth observed colour in the image.
4. Enter the ϵ=0.01.
5. Store index of processed pixel.
6. Calculate μci(x) =
1
∑ (‖x−vi‖
2
‖x−vj‖2)k
j=1
7. Calculate 𝒗𝒊 =∑ (𝜇𝑐𝑖
(𝑥))𝑚
× 𝑥𝑥∈𝑋
∑ (𝜇𝑐𝑖(𝑥))
𝑚
𝑥∈𝑋
8. If 𝒗𝒊𝒕+𝟏 − 𝒗𝒊
𝒕 ≥ 𝜀 then go to update step 6, else stop.
47
List 3.2 The algorithm for grouping objects by colour and location
List 3.1 shows the implemented algorithm of the fuzzy c-mean
clustering while the latter list (List 3.2) illustrates the proposed grouping
technique for better visualization of clustered objects. After this step, each
pixel is stored in a structure with its current index and the degree of
membership to each distinctive drawn coloured object.
Figure 3.5 The developed software showing fuzzy clustering effect in horizontal
orientation
1. Start with First Pixel in first row and first column.
2. Check each pixel RGB value in the 8 pixels neighbours
if having the same colour with 85% or above degree of
membership.
3. If true, group the pixel with its matched neighbours.
4. Increment the row count.
5. If the row count reached last row in the image,
increment the column number by 1
6. If column count reached last column in the image, goto
step 8.
7. Go to step 2.
8. Store each grouped pixels indices in an array.
48
Figure 3.6 The developed software showing fuzzy clustering effect in vertical
orientation
In Figure 3.5 and Figure 3.6, screenshots of the developed software
are presented which comprise two phases, the learning phase where
clustering iterations is performed, and the inspection phase where colour
grouping based on collected data is performed. The execution time is
calculated and displayed as well.
49
Features
Vector for
Line
s
Arcs Angle
Geometry Orientation
Pixel RGB
Colour
Space
Geometric Feature Extraction
4.1 Definition of feature vector
A feature vector is an n-dimensional vector of numerical features
that represent some object. Many algorithms in machine learning require
a numerical representation of objects, since such representations facilitate
processing and statistical analysis. When representing images, the feature
values might correspond to the pixels of an image, when representing texts
perhaps to term occurrence frequencies. In checking the quality of a drawn
object r, several features take place such as height (Hr), width (Wr),
orientation with respect to a predetermined reference (Ør) and colour
components in RGB format if the object is coloured. Figure 4.1 depicts the
visual representation of geometric features while Figure 4.2 illustrates the
classification of it.
Figure 4.1 Extracting geometric features
Figure 4.2 Feature vector as metrics for inspection
ØI
HI
WI
RGB
(Ri,Gi,Bi)
50
4.2 Features of interest in garnished wall plates
As shown in Figure 4.1 and Figure 4.2, inspecting garnished wall
plates involves measuring several features such as geometry (lines and
arcs), orientation of drawn objects with respect to reference (figure 4.1a)
and the colour of drawn object (discussed earlier in chapter 3). This
process in metrology is known as gaging. Selection of the appropriate
features has a drastic impact on the quality of the assessment [49] and
eventually the proper class of each ceramic plate.
For online inspection, due to the need of carrying out such a task
with the minimum time to avoid considering it as the bottleneck in the
production process, recent proposed systems carried its inspection only on
certain areas named Region Of Interest (ROI) [50].
4.3 Applied algorithm for preliminary locating of drawn
objects
To extract the feature needed to evaluate the geometry and
orientation of the studied ceramic plate, an edge detection process is
required which facilitates locating boundaries of drawn objects.
Methodology of edge detection
Edge detection is a fundamental tool in image processing and
computer vision, particularly in the areas of feature detection and feature
extraction, which aim at identifying points in a digital image at which the
image brightness changes sharply or more formally have discontinuities.
There are many methods for edge detection [51], but most of them
can be grouped into two categories. These are search-based and zero-
crossing based. The search-based methods detect edges by first computing
a measure of edge strength, usually a first-order derivative expression such
as the gradient magnitude, then searching for a local directional maximum
of the gradient magnitude using a computed estimate of the local
orientation of the edge, usually the gradient direction.
51
The zero-crossing based methods search for zero crossings in a
second-order derivative expression computed from the image in order to
find edges, usually the zero-crossings of the Laplacian or the zero-
crossings of a non-linear differential expression. As a pre-processing step
to edge detection, a smoothing stage, typically Gaussian smoothing, is
almost always applied.
Formulating edge detectors
If P(i, j) represents the intensity of the pixel P with the coordinates
(i, j), the pixels surrounding P(i, j) can be indexed as in Figure 4.3 (in case
of a 3 × 3 matrix):
P(i-1,j-1) P(i-1,j) P(i-1,j+1)
P(i,j-1) P(i,j) P(i,j+1)
P(i+1,j-1) P(i+1,j) P(i+1-1,j+1)
Figure 4.3 Processed pixel and its neighbours
A linear filter assigns to P(i, j) a value that is a linear combination
of its surrounding values. For example:
𝑷(𝒊,𝒋) = 𝑷(𝒊,𝒋−𝟏) + 𝑷(𝒊−𝟏,𝒋) + 𝟐𝑷(𝒊,𝒋) + 𝑷(𝒊+𝟏,𝒋) + 𝑷(𝒊,𝒋+𝟏) 4.1
A nonlinear filter assigns to P(i, j) a value that is not a linear
combination of the surrounding values. For example:
𝑷(𝒊,𝒋) = 𝒎𝒂𝒙(𝑷(𝒊−𝟏,𝒋−𝟏)
, 𝑷(𝒊+𝟏,𝒋−𝟏), 𝑷(𝒊−𝟏,𝒋+𝟏), 𝑷(𝒊+𝟏,𝒋+𝟏)) 4.2
So, For each pixel P(i, j) in an image, the convolution kernel
(Figure 4.4) is centred on P(i, j). Each pixel masked by the kernel is
multiplied by the coefficient placed on top of it. P(i, j) becomes either the
sum of these products divided by the sum of the coefficient or 1, depending
on which is greater [51].
K(i-1,j-1) K(i-1,j) K(i-1,j+1)
K(i,j-1) K(i,j) K(i,j+1)
K(i+1,j-1) K(i+1,j) K(i+1-1,j+1)
Figure 4.4 Kernel multiplied by the image window
52
The pixel P(i, j) is given the value :
(1
𝑁)∑𝐾(𝑎,𝑏)𝑃(𝑎,𝑏) 4.3
Where a is ranging from (i – 1) to (i + 1), and b is ranging from (j
– 1) to (j + 1). N is the normalization factor, equal to ∑ K(a, b) or 1,
whichever is greater.
If the new value P(i, j) is negative, it is set to 0. If the new value P(i,
j) is greater than 255, it is set to 255 (in the case of 8-bit resolution).
The greater the absolute value of a coefficient K(a, b), the more the
pixel P(a, b) contributes to the new value of P(i, j). If a coefficient K(a, b) is 0,
the neighbour P(a, b) does not contribute to the new value of P(i, j).
For example, if the convolution kernel is [0 0 0
−2 1 20 0 0
] , then:
𝑷(𝒊,𝒋) = −𝟐𝑷(𝒊−𝟏,𝒋) + 𝑷(𝒊,𝒋) + 𝟐𝑷(𝒊+𝟏,𝒋) 4.4
Filters are mainly used in edge detection, image blurring and
sharpening. Edge detectors also fall into two categories:
Highpass filters emphasize significant variations of the light intensity
usually found at the boundary of objects. Highpass filters help isolating
abruptly varying patterns that correspond to sharp edges, details, and
noise.
Lowpass filters attenuate variations of the light intensity. Lowpass
filters help emphasize gradually varying patterns such as objects and
the background. They have the tendency to smooth images by
eliminating details and blurring edges.
The edge detection methods that have been published mainly differ
in the types of smoothing filters that are applied and the way the measures
of edge strength are computed.
53
As many edge detection methods rely on the computation of image
gradients, they also differ in the types of filters used for computing
gradient estimates in x- and y-directions.
Tuned Prewitt Filter
The Prewitt edge detector is an appropriate way to estimate the
magnitude and orientation of an edge [51]. Although differential gradient
edge detection needs a rather time-consuming calculation to estimate the
orientation from the magnitudes in x- and y-directions, the Prewitt edge
detection obtains the orientation directly from the kernel with the
maximum response.
(a) Original Image (b) Prewitt Filter
(c) Feature Extraction
Figure 4.5 Edge detection and extracting geometry of drawn objects
Let 𝑃(𝑖,𝑗) be the pixel located at ith row and jth column, moving a
mask of size 3X3 pixels along the image and calculate the new pixel value
in centre where mask window is applied using the formula:
𝑷(𝒊,𝒋) = 𝒎𝒂𝒙
[ |𝑷(𝒊+𝟏,𝒋−𝟏) − 𝑷(𝒊−𝟏,𝒋−𝟏) + 𝑷(𝒊+𝟏,𝒋) −
𝑷(𝒊−𝟏,𝒋) + 𝑷(𝒊+𝟏,𝒋+𝟏) − 𝑷(𝒊−𝟏,𝒋+𝟏)| ,
|𝑷(𝒊−𝟏,𝒋+𝟏) − 𝑷(𝒊−𝟏,𝒋−𝟏) + 𝑷(𝒊,𝒋+𝟏) −
𝑷(𝒊,𝒋−𝟏) + 𝑷(𝒊+𝟏,𝒋+𝟏) − 𝑷(𝒊+𝟏,𝒋−𝟏)|]
4.5
The acquired image is transformed to grey scale image as discussed
in section 2.2.1. Thereafter, a mask of 3𝑥3 aka kernel is applied around
each drawn object where its centre initially located in the colour grouping
stage using the fuzzy clustering.
54
This way, most of the time needed to locate edges by moving the
mask all over the image is saved which is meant to tune the usage of such
a filter.
On the contrary of the time consuming correlation technique that
requires intensive computations, edge detection can serve as a preliminary
locator for each shape. By comparing each pixel with higher grey value
(edge) to its neighbours, Points at each edge of the drawn object can be
found. A rake of parallel lines is used to measure the linear dimensions of
each side.
Figure 4.5b shows the ceramic plate after applying Prewitt filter.
Searching for the four edges is fairly trivial by starting at any point in the
boundary of any edge and searching for points where 2 points of its
surrounding 4 adjacent points is below a chosen threshold of 120 on the
grey scale levelling based trials and error. Edges on the same line are used
to determine the orientation of the rake used to retrieve the dimension and
linearity of that line (Figure 4.5c). This is done as the rake consists of
parallel lines which once intersect with any edge, a point is drawn.
The points collected from each line can evaluate the linearity of the
line by finding the best fit and calculating the error along the fitting
equation as shown in Figure 4.6. Fitting plays a major rule in noise
rejection characterised by small difference values (less than 2).
Figure 4.6 Fitting detected points along the profile into lines
Profile length
Edg
e D
iffe
ren
ce
55
4.4 Computing Feature Score
After feature extraction stage, it is demanded to evaluate the
parameters extracted which are summarized in two main categories:
Colour Quality.
Accuracy of geometry (dimension, distance from reference
and orientation).
Calculating the score for colour quality
Let the drawn object detected by the fuzzy c-clustering
(section 3.7.3) be annotated by Oi where i represents the index of the drawn
object in the ceramic plate under inspection. The drawn object has Pm
pixels within its boundaries where every pixel has μm degree of
membership to the expected colour of the drawn object. The colour score
(CS) of the drawn object i is calculated as follows:
𝐶𝑆𝑂𝑖=
∑ 𝜇𝑚𝑚𝑘=1
𝑚 4.6
Where m is the total number of pixels in every drawn object. The
overall score is (CSoverall) is then calculated by evaluating the average of
the colour scores:
𝐶𝑆𝑜𝑣𝑒𝑟𝑎𝑙𝑙 =∑ 𝐶𝑆𝑘
𝑖𝑘=1
𝑖 4.7
Calculating the accuracy of geometric features
As mentioned earlier (Section 4.2), the geometric feature
extraction involves checking for dimension, distance from the reference
point (shown in Figure 4.7) and expected orientation with respect to this
reference.
Once every dimension is retrieved after fitting operation, the
Dimension Score (DS) of each object is calculated as:
𝐷𝑆𝑂𝑖=
∑ 1−|𝐷𝑡𝑘
−𝐷𝑚𝑘|
𝐷𝑡𝑘
𝑛𝑘=1
𝑛 4.8
56
Where n is the total number of extracted dimensions. The overall
score is then calculated as:
𝐷𝑆𝑜𝑣𝑒𝑟𝑎𝑙𝑙 =∑ 𝐷𝑆𝑘
𝑖𝑘=1
𝑖 4.9
The same steps are carried out to evaluate the score of distance of
each object’s centre from the reference which is annotated by (LS) and the
orientation of each object (OS). The overall geometry score (OGS) is then
calculated as:
𝑂𝐺𝑆 =𝐷𝑆𝑜𝑣𝑒𝑟𝑎𝑙𝑙+𝐿𝑆𝑜𝑣𝑒𝑟𝑎𝑙𝑙+𝑂𝑆𝑜𝑣𝑒𝑟𝑎𝑙𝑙
3 4.10
Figure 4.7 Screenshot of the developed software showing the feature extraction stage
The screenshot in Figure 4.7 shows the developed software which
highlights the boundaries of each drawn object, calculates its dimension
and orientation with respect to predetermined reference shown in the lower
left corner.
57
Highlighting is made using three layers:
Red layer: for highlighting geometry of each drawn object.
Green layer: for connecring the reference point to the centre
of each drawn object to calculate the orientation.
Yellow layer: for pointing to the rejected objects that doesn’t
match expectd dimension, colour or orientation. These objects
mainly reside near borders (incomplete shape).
The performance of the algorithm in terms of time consumed to
extract features covering geometry and orientation is also displayed.
58
Classification of ceramic plates using fuzzy logic
After extracting the geometric features and collecting colour
information of each drawn object, classification stage comes to place by
means of stating the rules for assessment based on fuzzy logic.
Fuzzy logic and fuzzy logic systems
Fuzzy logic is a form of multi-valued logic derived from fuzzy set
theory to deal with reasoning that is approximate rather than accurate. In
contrast with "crisp logic", where binary sets have binary logic, fuzzy logic
variables may have a truth value that ranges between 0 and 1 and is not
constrained to the two truth values of classic propositional logic.
Furthermore, when linguistic variables are used, these degrees may be
managed by specific functions [52].
Fuzzy logic (FL) was first proposed by Zadeh in 1965 [53]. Though
fuzzy logic has been applied to many fields, from control theory to
artificial intelligence, it still remains controversial among most
statisticians, who prefer Bayesian logic, and some control engineers, who
prefer traditional two-valued logic. The term given to a system of
mathematics developed to model the human brain’s curious way of
processing words. The main motivation behind FL was the existence of
imprecision in the measurement process. Zadeh explains that ‘‘As
complexity rises, precise statements lose meaning and meaningful
statements lose precision”.
FL provides capabilities that allow handling both quantitative and
qualitative information within one model. It is a form of multivalued logic
derived from fuzzy set theory to deal with reasoning that is approximate
rather than precise. Fuzzy sets are sets whose elements have degrees of
membership. In classical set theory, the membership of elements in a set
is assessed in binary terms according to a Boolean condition—an element
either belongs or does not belong to the set. By contrast, fuzzy set theory
permits the gradual assessment of the membership of elements in a set; this
is described with the aid of a membership function valued in the real unit
interval [0, 1].
59
Fuzzy sets generalize classical sets, since the indicator functions of
classical sets are special cases of the membership functions of fuzzy sets,
if the latter only take values 0 or 1 [54]. Formally, a fuzzy set A in U is
expressed as a set of ordered pairs:
𝐴 = (𝑥, 𝜇𝐴(𝑥))|𝑥 𝑖𝑛 𝑈} 5.1
Where U is the universe of discourse, 𝜇𝐴(𝑥) is the membership
function (MF) that portrays the degree of membership of x; it indicates the
degree to which x belongs in set A. The list of common MFs that are used
often includes triangular MFs, trapezoidal MFs, Gaussian MFs, and
generalized bell MFs [55].
Just as in fuzzy set theory the set membership values can range
(inclusively) between 0 and 1, in fuzzy logic the degree of truth of a
statement can range between 0 and 1 and is not constrained to the two truth
values (true, false) as in classic predicate logic. When qualitative values
are used, these degrees may be managed by specific inferential procedures.
Fuzzy logic allows variables to take on qualitative values which are words.
For example, the size variable may assume qualitative values of, e.g., very
large, large, small, and very small. Each qualitative value is associated
with a fuzzy set, each of which has a defined MF.
Figure 5.1 An eample of membership funcrion for evaluation of a program
Figure 5.1 shows an example for evaluating the developed software
in terms of the number of lines written in the program. The figure
illustrates the linguistic values used in assessment namely ”low”, medium”
and ‘‘high”. Each of these linguistic values is associated with a fuzzy set
defined by a corresponding membership function.
60
A singleton fuzzy set is a fuzzy set with exactly one element with
full membership of 1. For example, the set 𝐴 = (13, 1) is a singleton.
Fuzzy Logic System (FLS) is the name given to any system that has a
direct relationship with fuzzy concepts (e.g., fuzzy sets and quantitative
values) and fuzzy logic. The most popular fuzzy logic systems in the
literature may be classified into three types, pure fuzzy logic systems,
Takagi and Sugeno’s fuzzy system, and fuzzy logic system with fuzzifier
and defuzzifier.
As most of the engineering applications use crisp data as input and
produce crisp data as output, the last type is the most widely used one
where the fuzzifier maps crisp inputs into fuzzy sets and the defuzzifier
maps fuzzy sets into crisp outputs. This type of fuzzy logic system was
first proposed by Mamdani [56]. It has been successfully applied to a
variety of industrial processes and consumer products.
Mamdani’s fuzzy logic system
The four main components of Mamdani’s FLS are briefly described
as follows:
5.2.1 Fuzzification
Fuzzification is the process of converting inputs into their fuzzy
representations.
For example, it converts crisp inputs into fuzzy sets such as
converting a colour score of 0.1 into [Bad, 0.8] and [Good, 0.2] where the
first parameter (Good, Bad) depicts the linguistic value and the latter
(0.1,0.9) are the degree of membership to that value.
Figure 5.2 demonstrates the process of fuzzifying the extracted
features (colour and geometry) in order to be assessed by the proposed
fuzzy rules.
61
Figure 5.2 Fuzzifying ceramic plates features vector
5.2.2 Rules
Rules are the heart of the FLS, and may be provided by experts or
can be extracted from numerical data. In either case, the rules are typically
expressed as a collection of fuzzy IF-THEN rules. The IF-part of a rule is
its antecedent, and the THEN-part of the rule is its consequent. Fuzzy sets
are associated with terms that appear in the antecedents or consequents of
rules, and with inputs to and output of the FLS. A fuzzy IF THEN rule is
of the form
𝐼𝐹 𝑋1 = 𝐴1 𝑎𝑛𝑑 𝑋2 = 𝐴2 𝑎𝑛𝑑 … . 𝑋𝑛 = 𝐴𝑛 𝑇𝐻𝐸𝑁 𝑌 = 𝐵 , 𝑛 = [0,1] 5.2
𝐼𝐹 𝑋1 = 𝐴1 𝑜𝑟 𝑋2 = 𝐴2 𝑜𝑟 … . 𝑋𝑛 = 𝐴𝑛 𝑇𝐻𝐸𝑁 𝑌 = 𝐵 , 𝑛 = [0,1] 5.3
Where Xn and Yn are linguistic variables, A and B are linguistic
values represented by fuzzy sets. The IF part is the antecedent or premise,
while the THEN part is the consequent or conclusion. An example of a
fuzzy IF-THEN rule is:
𝐼𝐹 𝑆𝑖𝑧𝑒 = 𝐿𝑜𝑤 𝑇𝐻𝐸𝑁 𝐸𝑓𝑓𝑜𝑟𝑡 = 𝐻𝑖𝑔ℎ 5.4
In a fuzzy logic system, the collection of fuzzy IF-THEN rules is
known as fuzzy rule base.
5.2.2.1 Fuzzy operations
Since fuzzy logic has a degree of membership varied between zero
and one rather than crisp value representing total truth or total false, using
the logical operators (AND, OR and NOT) has a more generic rule in
implementation stated in the following points:
Bad=0.8
Good=0.2
Geometric
Score=0.7
Tolerated=0.6
Accurate=0.4
Colour
Score=0.1
(a) (b)
62
AND operator: which is evaluated as either the minimum or
the product of the given two linguistic values.
OR operator: which is calculated as the maximum of the given
two linguistic values. Another rarely used technique is the
probabilistic OR given as A+B-A.B.
NOT operator: which is a unary operator where it extracted
the complement of the linguistic value.
Figure 5.3 depicts an example of using the various fuzzy logical
operations based on the extracted colour and geometric scores obtained
from the assessment of the garnished ceramic plate under investigation.
Figure 5.3 Example of implementing fuzzy logical operations
Colour Score =0.7
Geometric Score =0.3
IF Colour Score=A1 and Geometric Score=A2
ANDmin. Result=0.3
Colour Score =0.7
Geometric Score =0.3
ANDprod. Result=0.21
IF Colour Score=A1 or Geometric Score=A2
Colour Score =0.7
ORmax. Result=0.7
Colour Score =0.7
Geometric Score =0.3
ORprob. Result=0.79
Geometric Score =0.3
IF NOT Colour Score=A1
Colour Score =0.7 NOT Result=0.3
63
The selection of a method over another is either inferred from
expert system of the domain knowledge or by means of trials and error.
5.2.2.2 Implication
Implication is meant to propagate the result of the antecedent (the
IF part) to the consequent (the THEN part). In other words, it is the process
of reshaping the output membership function. Meanwhile, rules can be
given a weight (from 0 to 1) which may affect the implication process,
however, it is rarely used [57].
Figure 5.4 shows an example of the implication process used to
evaluate one of the proposed rules in the ceramic classification system
(will be stated later in section 5.4). The colour score and geometry score
(discussed earlier in section 4.4) are first fuzzified to represent a degree of
membership.
Figure 5.4 An implication example from the proposed rules
The Antecedent part is then evaluated using the product technique
of AND operation and the result eventually propagate to the colour spot
membership function to reshape it.
5.2.2.3 Aggregation
Because decisions are based on the testing of all of the rules in a
Fuzzy Inference System (FIS), the rules must be combined in some manner
in order to make a decision.
Aggregation is the process by which the fuzzy sets that represent
the outputs of each rule are combined into a single fuzzy set. Aggregation
only occurs once for each output variable, just prior to the final step,
defuzzification.
Antecedent
Colour
Score=0.279
(DOM=0.558)
Consequent
Possibility of Colour
Spot=0.187
Geometry
Score=0.168
(DOM=0.336)
IF (Colour is Bad) AND (Geometry is Inaccurate) THEN
(Ceramic Plate has Color Spot)
64
The input of the aggregation process is the list of truncated output
functions returned by the implication process for each rule. The output of
the aggregation process is one fuzzy set for each output variable [57].
As long as the aggregation method is commutative, then the order
in which the rules are executed is unimportant. Three common methods
are used:
Maximum
Probabilistic OR
Summation of each rule’s output set.
Figure 5.5 Example of aggregating effect on the possibility of accepting the ceramic
plate
5.2.3 Defuzzification
The defuzzifier converts the output of the inference engine into
crisp output. It is needed in many applications where crisp numbers must
be obtained as output.
The input for the defuzzification process is a fuzzy set (the
aggregate output fuzzy set) and the output is a single number. As much as
fuzziness helps the rule evaluation during the intermediate steps, the final
desired output for each variable is generally a single number.
Ag
gre
gat
ion
Implication
Colour Score=0.8 Geom. Score=0.75 Acceptable=0.33
65
However, the aggregate of a fuzzy set encompasses a range of
output values, and so must be defuzzified in order to resolve a single output
value from the set [57].
5.2.3.1 Defuzzification techniques
Three techniques are used for diffuzification:
5.2.3.1.1 Centroid
Centroid defuzzification returns the centre of area under the curve.
If the area is considered as a plate of equal density, the centroid is the point
along the x axis about which this shape would balance. This is considered
the most used technique [54, 58-61].
5.2.3.1.2 Bisector
The bisector is the vertical line that will divide the region into two
sub-regions of equal area. It is sometimes, but not always coincident with
the centroid line.
5.2.3.1.3 Middle, Largest and Smallest of Maximum
MOM, SOM, and LOM stand for Middle, Smallest, and Largest of
Maximum, respectively. These three methods key off the maximum value
assumed by the aggregate membership function. If the aggregate
membership function has a unique maximum, then MOM, SOM, and LOM
all take on the same value.
A visual evaluation is shown in Figure 5.6 where the outcome of
each technique (crisp value annotated by a line and the name of the
method) is displayed. The selection of the appropriate method depends on
the application and expected result obtained from the domain expert as
stated in [55, 62, 63].
Methodologies utilized in building a fuzzy inference
system
Fuzzy logic forms an effective technique of combining linguistic
and numerical information, which can be done in two ways:
66
Use of linguistic information (experts’ knowledge) to develop an initial
fuzzy logic system, and then adjust the parameters of the initial fuzzy
logic system by using on numerical information.
Use of numerical information and linguistic information to develop
two separate fuzzy logic systems, and then final fuzzy logic system is
obtained by averaging them.
Figure 5.6 Result of different defuzzification techniques
In the first case, components of the FLS are set based on the
expert’s opinion. These components include number of rules, shape and
position of the membership functions and the shape and position of the
consequents etc.
Historical data is then used to further tune the parameters of the
fuzzy logic system. The second way of coming up with adaptive FLS is
straight forward in the sense that once the parameters of the individual
systems are available, one can average these parameters to produce a final
FLS. Eventually, the overall fuzzy system would comprise all components
illustrated in Figure 5.7.
.
Aggregated value
Deg
ree
of
mem
ber
ship
67
Figure 5.7 Fuzzy Logic System
Formulating the proposed fuzzy classifier for the
assessment of garnished wall plates
Mamdani’s inference engine was chosen to be the core inference engine.
Figure 5.8 Screenshot of the developed Mamdani’s inference engine for ceramic
assessment
Fuzzification
Crisp input values
Fuzzy Inference Engine
Defuzzification
Crisp output values
Fuzzy Rule
Base
68
The following sections describe the flow of the proposed FIS.
MATLAB version 2011a has a fuzzy logic toolbox with visual designer
for implementation of the fuzzy classifier as shown in Figure 5.8
The proposed fuzzy classifier has two input variables (colour and
geometric score) used in evaluating the possibility of three output grades
(acceptable, colour mismatch and colour spot) in order to determine the
final grading of the garnished ceramic wall plate.
5.4.1 Fuzzification of input and output
Two input parameters were selected to be the input of the fuzzifier,
namely colour and geometric scores obtained from the feature extraction
process (section 4.4). Each score spans from zero to one which offers
normalized range at no extra calculations. For colour score, the distribution
of each linguistic value is shown in Figure 5.9 while the range is tabulated
in Table 5.1. The same is stated in Figure 5.10 and Table 5.2 for the
developed geometric score membership function.
Table 5.1 Range of colour score
Figure 5.9 Colour score membership function
Table 5.2 Range of geometric score
Figure 5.10 Geometric score membership function
Linguistic Value Range
Bad 0-0.5
Good 0-1
Shiny 0.5-1
Linguistic Value Range
Inaccurate 0-0.5
Tolerated 0-1
Accurate 0.5-1
69
The proposed output linguistic variables were taken from the common
problems encountered in ceramic inspection which are colour spots and
colour mismatch. They are represented by ramp line to reflect the
possibility of grading from zero to hundred percent as presented in
Figure 5.11. Grading was normalized (from zero to one) where zero sands
for zero percent and one stands for hundred percent.
Figure 5.11 The output membership functions of ceramic grading
5.4.2 Rules for classification
Nine rules were modelled and inferred from the domain expert for
human classification of the garnished plates. These rules are based on the
fuzzified inputs of colour and geometric scores.
Rules are arranged as shown in Table 5.3. The intersected cell
between colour score linguistic values and the geometric score linguistic
peers forms the overall classification results. For instance:
𝐼𝐹 𝐶𝑆 𝑖𝑠 𝐺𝑜𝑜𝑑 𝑎𝑛𝑑 𝐺𝑆 𝑖𝑠 𝐴𝑐𝑐𝑢𝑟𝑎𝑡𝑒 𝑇𝐻𝐸𝑁 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑠 𝐴𝑐𝑐𝑒𝑝𝑡𝑎𝑏𝑙𝑒 𝐼𝐹 𝐶𝑆 𝑖𝑠 𝐵𝑎𝑑 𝑎𝑛𝑑 𝐺𝑆 𝑖𝑠 𝑇𝑜𝑙𝑒𝑟𝑎𝑡𝑒𝑑 𝑇𝐻𝐸𝑁 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑠 𝐶𝑜𝑙𝑜𝑢𝑟 𝑀𝑖𝑠𝑚𝑎𝑡𝑐ℎ
Table 5.3 Rules for classifying garnished plates
Geometric Score
Inaccurate Tolerated Accurate
Colour
Score
Bad Colour
Spot
Colour
Mismatch
Colour
mismatch
Good Colour
Spot Colour Spot Acceptable
Shiny Colour
Spot Acceptable Acceptable
70
The three linguistic values (bad, good and shiny) describing the
quality of colour is combined with the geometric score values (inaccurate,
tolerated and accurate) to conclude the classification grade of the garnished
ceramic plate (acceptable, colour spot and colour mismatch) based on the
specified rules.
Following the trials and error for selecting the appropriate
parameters of the fuzzy classifier, the AND operation in applying rules
was carried out using the product technique, implication was applied based
on minimum method.
5.4.3 Proposed modified defuziification method
The smallest of the maximum (SOM) method proved to yield better
results during the tuning stage of the fuzzy classifier. Since three
possibilities are given after the process of defuzzification (acceptable,
colour spot and colour mismatch), the final grade of the ceramic plate is
brought in by finding the maximum among the three crisp values. In other
words:
𝐶𝑒𝑟𝑎𝑚𝑖𝑐 𝐺𝑟𝑎𝑑𝑒 = max(0≤𝐶𝑉≥1)
(𝐶𝑉1𝐶𝑉2, 𝐶𝑉3) 5.5
Where (CV1, CV2 and CV3) stands for the crisp value gained after
defuzzifying the acceptable, colour spot and colour mismatch membership
functions respectively.
71
Results and Discussion
In this chapter, results of the conducted research is discussed where
several tests including statistical analysis were carried out to study the
impact of the proposed inspection system on productivity and to evaluate
the overall efficiency.
6.1 Implementation of the proposed system
A prototype was designed in campus at Misr University for Science
and Technology to emulate an industrial vision system. A Charge Coupled
Device (CCD) firewire colour camera is used for image acquisition with
resolution of 640X480 and shutter speed of 60 fps. Two flexible arms are
used to adjust illumination to minimise reflectivity due to nature of
ceramic surface. Black curtain is utilised to isolate inspection environment
from external illumination along the day to study the illumination
variability based on the two flexible arms carrying the light source. The
prototype is controlled by a USB interfacing circuit. An offline experiment
was performed by placing manually each plate.
6.1.1 Mechanical Design
The design procedure was carried out using the commonly used
Computer Aided Design (CAD) software SolidWorks 2009. The design
was meant to create a generic flexible cell for quality inspection of various
products to serve as a pillar for further research activities in automated
inspection. An iterative design model has been applied to modify design
drafts after manufacturing.
a) Mechanical drawing b) Implantation
Figure 6.1 The test rig of the proposed system
72
As shown in Figure 6.1, a carriage is transferred to each station via
ball screw. The carriage acts as an automatic feeder using series of
successive rollers.
6.1.2 Electrical Circuitry
Two DC motors took place in the process of the FMC control
approach. The first is responsible for feeding the ceramic plates under
inspection, and the second one transfers the carriage to each classification
station (three stations).
a) Schematic design b) Circuit Layout
c) Testing motors direction
Figure 6.2 the H-Bridge circuit
73
An H-Bridge circuit is responsible for controlling the direction of
each motor by changing the polarity of terminal DC voltage applied to each
motor. Eagle PCB software was chosen to design the schematic diagram
and PCB layout.
Figure 6.3 Schematic diagram of the USB interfacing card
Figure 6.4 PCB layout of the interfacing card
74
The chosen Microcontroller is Microchip PIC16F877 and the USB
functionality was added by utilization of FTD485 First in First out (FIFO)
USB to Parallel chip as shown in Figure 6.3 and Figure 6.4.
6.1.3 Software Architecture
Reusability and rapid application development (RAD) were the
cornerstones in software design phase. The key pillars of software entities
comprises: Fuzzy C-Mean calculations, Fuzzy Logic Inference System,
Image Processing and FMC control.
Mathworks MATLAB 2011a software has a Fuzzy logic toolbox
(as presented in chapter 5) provides a very efficient way for fuzzy
clustering and Fuzzy logic implementation. Meanwhile, Microsoft Visual
Basic.NET and .NET framework were used to build robust classes for
FMC control and image processing. MATLAB developed functions
(fuzzy c-mean and fuzzy logic) and Visual Basic.NET was linked by
means of MATLAB for .NET builder provided in MATLAB as
deployment tools to allow interoperability with other software vendors.
The procedure is simply after developing the function (called m-
file in MATLAB), the MATLAB for .NET builder produces a DLL file
with wrapper class for the mentioned MATLAB functions that can be
integrated easily with any language based on Microsoft .NET framework
(like C# or VB). MATLAB Component Runtime handles the
communication between MATLAB and VB.NET.
Figure 6.5 Architectural design of the developed software
MATLAB
Fuzzy Logic
Classifier
Fuzzy C-Mean
Clustering
Visual Basic.NET
Communication with
interfacing circuit
Image Processing
User Interface .NET builder
MATLAB
Component Runtime
(MCR)
75
6.2 Results
Images acquired using Basler A601fc CCD firewire camera was
subjected to variability in illumination. Ceramic size of 200mm X 150mm
was used during experiment. Results obtained from automated system
were compared to knowledge of experienced worker.
Figure 6.6 Rotated garnished ceramic plate
Figure 6.7 Ceramic plate with colour mismatch
76
Figure 6.8 Ceramic plate with colour spots
The above figures (Figure 6.6, Figure 6.7 and Figure 6.8) show an
example of the acceptable plate, plate with colour mismatch and a plate
with colour spot respectively. Table 6.1 shows the results of the
classification algorithm proposed in the thesis.
Table 6.1 Result of the proposed classification system for the stated figures
Fig.
No.
Colour
Quality
Accuracy
of
Geometry
Acceptable Colour
Mismatch
Colour
Spot
6.6 0.61 0.68 0.29 0.0 0.5
6.7 0.12 0.8 0.15 0.46 0.1
6.8 0.92 0.85 0.59 0.0 0.05
The system was tested over 35 images with prior knowledge of
their classification. An aggregated result depicting the overall assessment
of the proposed system presented in Table 6.2. The accuracy is calculated
as follows:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (%)𝑐𝑙𝑎𝑠𝑠 = 1 − (𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝐹𝑜𝑢𝑛𝑑 𝑃𝑎𝑡𝑡𝑒𝑟𝑛𝑠−𝐴𝑐𝑡𝑢𝑎𝑙 𝐹𝑜𝑢𝑛𝑑 𝑃𝑎𝑡𝑡𝑒𝑟𝑛𝑠
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝐹𝑜𝑢𝑛𝑑 𝑃𝑎𝑡𝑡𝑒𝑟𝑛𝑠) 6.1
77
Table 6.2 Accuracy of automatic detection of correct classes
An experiment had been conducted to study the effect of the
rotation over accuracy of ceramic classification. Results are charted in
Figure 6.9. The horizontal axis represents the orientation of the drawn
object with respect to the master ceramic plate orientation. The vertical
axis shows the detection rate in percentage.
Figure 6.9 The effect of rotating ceramic plates over correct detection of its class
A comparative study was also carried out to investigate the impact
of utilization of fuzzy c-mean over the traditional k-nearest neighbour
clustering to perform colour grouping (chapter 3).
In Nearest Neighbour classification, the distance of an input colour
vector X = [𝑥𝑖𝑟 , 𝑥𝑖𝑔 , 𝑥𝑖𝑏] (as discussed in section 3.6) of unknown class to
a class Cj is defined as the distance to the closest sample that is used to
represent the class. In other words:
d(X, Cj) = mini
d(X, Xij) 6.2
Type Automatic Detection
Acceptable 95.3 %
Colour Spot 97.3%
Colour Mismatch 91.6 %
Total Accuracy 94.7 %
0 50 100 150 200 250 30050
60
70
80
90
Drawn Objects Orientation (in degrees)
Co
rerc
ted
Det
ecti
on
78
Fuzzy C-Means
Correct Match(94%)
Incorrect Match(6%)
Nearest Neighbour
Correct Match(67%)
Incorrect Match(33%)
Where 𝑑(𝑋, 𝑋𝑖𝑗) is the distance between X and 𝑋𝑖
𝑗. Distance is
calculated by means of Euclidean Distance:
d(X, Y) = √(∑ (Xi − Yini=1 )2 6.3
Each cluster is determined by the averaging the colour dominating
each drawn object. The below figure depicts results of colour based
learning using both fuzzy c-means and nearest neighbour.
a) Correct classification using nearest neighbour colour grouping approach
b) Correct classification using fuzzy c-mean colour grouping approach
Figure 6.10 Detection percentage of ceramic grade (nearest neigbour vs. fuzzy c-mean
One of the worthy observations is although several papers came
with an interesting approach for tiles inspection [12, 27, 29, 30, 64, 65];
most of it ignored the execution speed which holds a paramount effect in
implementation.
Since one of the challenges facing such a system is performing the
inspection process while conveyor is moving at its nominal speed to avoid
delaying production time. As stated earlier, most of ceramic production
lines operate at high speed.
79
There are key factors that can be good candidates as metrics for
ceramic inspection systems such as software decision speed, signal
transmission rate between host controller and the Mechatronic sorting
system and mechanical actuation speed which in other words are
interpreted as how fast the actuation system responds to decision obtained
from vision software.
The Below performance metrics were recorded using tower PC
with Pentium 4 processor and 1 GB RAM running windows XP:
Table 6.3 Selected mterics for software performance assessment
Table 6.4 System configuration
6.3 Discussion
Developing an inspection system for garnished ceramic plates has
many challenges to tackle. Image acquisition may affect results in several
ways. One of the common problems is lens distortion. Distorted images
yields erroneous measurements as it provides false information in regard
to angles and geometry.
To correct the acquired image, a hardware solution commonly
takes place via utilisation of telecentric lenses.
Criteria Average Time
Image acquisition 16.67 ms
Gamma correction 23 ms
Image calibration 86 ms
Prewitt filter 15 ms
Geometric feature extraction 120 ms
Fuzzy clustering 15 Sec.
Evaluation of fuzzy rules 69 ms
Criteria Specification
USB actual transfer speed 1MB / S
Inspection Area 20 X 20 cm2
80
Even though telecentric lenses may add an extra cost to the imaging
system since it may cost more than the imaging device itself, it is
considered a perfect choice for small features measurements and detection
such as cracks that may require high resolution cameras with error free
lenses.
The second remedy for lens distortion is by using software
calibration, which involves mapping each pixel to a new location based on
calibrated grid. Therefore, as illustrated in section 2.2 , a proposed soft
calibration algorithm was introduced as an attempt to remove or at least to
minimise that distortion using a calibration grid and the illustrated
developed algorithm. A visual representation of the corrected image of the
garnished plate showed an enhanced image as a proof of geometric shape
restoration of drawn objects.
Even though such a proposed algorithm is considered an economic
solution with promising results as demonstrated in thesis, tradeoffs in
measurements due to small deviation based on interpolation between grids
to estimate the correct image profile should be put into consideration.
Observing the variability in correct pattern classification can be
justified if the effect of rotation ceramic plates was carefully considered as
the soft calibration won’t be able to provide same accuracy as the
telecentric length. The other factor is the illumination which is hard to be
uniform due to severe reflectivity of ceramic surfaces. The overall
accuracy of the inspection system is summarised in Table 6.2.
.
Based on experimentation carried out, Fuzzy c-mean yielded better
results than nearest neighbour technique. The result is rational since the
centre of each cluster is modified in every iteration. Nearest Neighbour
would be adequate whenever one of the features of each class has no
chance to overlap with the other. Keeping this notion in mind, the choice
of fuzzy logic classifier to mimic human reasoning was superior over other
techniques in classification based on geometry and colour.
81
Conclusion
The presented work was aimed to develop and implement a flexible
manufacturing cell to inspect garnished ceramic wall plates. We may
summarize the work carried out as follows:
1. A proposed calibration algorithm was to correct the lens
distortion by means of software.
2. A new visualization approach for colour grouping was also
proposed using fuzzy c-mean clustering technique.
3. A fuzzy inference engine was built to classify the garnished
ceramic plates into three common categories (acceptable,
ceramic plate having colour spots, and ceramic plate having
colour mismatched drawn objects) based on calculated
geometric and colour score from a feature extraction process.
4. A test rig was developed to emulate the production
environment.
5. The system shows promising results in terms of accuracy in
correct classification and withstanding against variability in
illumination distribution.
6. The system is considered novel compared to other published
work since it is the only work which considered geometric
features of drawn objects up to the time of submitting this
thesis.
‘
82
Future work
The conducted thesis could be expanded in the future to cover:
The 3D inspection of surface roughness using stereoscopic
vision systems.
Utilization of parallelism and multicore technology in pattern
concurrency.
The effect of classification by other methodologies such as
Support Vector Machine, Particle Swarm Optimization and
modern published algorithms.
An improved mechanical mechanism for speeding up the
sorting process.
83
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Published Papers
1. A. M. Aly and W. A. El-Badry, "Design and Implementation of
Flexible Manufacturing Cell for Quality Inspection of Garnished Ceramic
Wall Plates," in 19th Conference of French Congress of Mechanics
Marseille, France, 2009.
2. F. A. Tolbah, A. M. Aly, and W. A. El-Badry, "Automated grading
system for garnished wall plates: A mechatronic approach," presented at
the 8th International Conference on Production Engineering and Design
for Development, Cairo, Egypt, 2010.
3. F. A. Tolba, A. M. Aly, and W. A. El-Badry, "An Enhanced Vision
System for Sorting Ceramic Plates Based on Hybrid Algorithm and USB
Interfacing Circuitry," presented at the 27th National Radio Science
Conference, Menoufia, Egypt, 2010.
90
الرسالة ملخص
حائطبالط الإن الُجهدَ البحثّي المبذول للوصول إلى حّل موضوعّي لمشكلة فحص وتحليل وتمييز
يمكن تبريره بسهولة بالمكاسب التجارية وأمن االستخدام اللذْين يعودان على الصناعة الخزفّي المزخرف
الط انس ممكن ألنواع البعند تحويل نُُهج الفحص اليدوي العتيقة إلى طرائق آليّة، مما ينتج عنه أعلى تج
المنتج الموجود في المخازن، مما يؤدي في النهاية إلى ثبات عمليات المعالجة والفحص الشاملة.
ومن المشاكل العامة في عملية الفحص اليدوية اإلجهاد وضعف التركيز البشري، مما يؤدّي إلى
لمعروف أن التغير التدريجّي فيفي المصنع. ومن ا المزخرف أخطاء في عملية تصنيف البالط الخزفيّ
األلوان يصعب على الفاحص البشرّي اكتشافه، ومن المحتمل أن التغيرات الطفيفة المتدرجة والمستمرة
ال يمكن مالحظتها في مراحل مبكرة. وباإلضافة إلى ذلك، فإنه يصعب على العين البشرية على وجه
لواحد، عندما تتغير أحوال اإلضاءة أثناء العمل في الخصوص أن تفرز البالط بدقة طبقا لدرجات اللون ا
مكان الفرز )المصنع أو ملحقاته(. ويتأثر الحكم البشرّي، كالمعتاد، بمستوى التوقعات والمعرفة المسبقة.
ومنذ أوائل التسعينيات، نشرت أبحاث كان محور اهتمامها التوّصل إلى طرائق جديدة الكتشاف
التشققات الدقيقة وتبقُّع األلوان في البالط الخزفّي. وعلى أية حال، كانت هذه البحوث منصبّة على التوّصل
لّي )مثل تغيّر أحوال إلى خوارزميّة فحص مناسبة وأهملت الجوانب األخرى المتعلقة بنظام الرؤية اآل
اإلضاءة وكذا عاكسيّة سطح البالطة المفحوص، ونظام حيازة الصورة، والنظام الميكانيكّي لمناولة البالط
والدارة الكهربيّة للمواجهة البينيّة(. وفي نفس الوقت، لم يسبق وجود نظام يقيس األشكال الهندسية
ه، قبل االضطالع بتبعات وأعباء عملنا البحثّي هذا، المرسومة وجودة األلوان، وال حتى مناقشة موضوع
الخزفّي المزخرف. بالط الحائطفحص والذي نرى أنه ال بدّ منه عند
الخزفّي بالط الحائطفحص تعرض األطروحة الحالية المقترحة مدخال جديدا إلى حل مشكلة
يعتمد على كل مطّور م هجيننظابالنظر إلى الشكل الهندسّي واأللوان الناتجة، مؤسسا على المزخرف
ان الكتشاف التشققات الدقيقة وتبقّع األلوسي" –من منطق الحاالت الغامضة وطريقة "التعنقد بالمتوسط
تقييميّة لألشكال الهندسية واختالفات درجة اللون. كما يؤّمن هذا النظام المقترح كذلك إعطاء درجات
ومالمح األلوان النهائية.
لمعامالت موضع النقاش الخاصة بنظام حيازة الصور بالتفصيل مع تغيير هذه وقد تمت مناقشة ا
المعامالت المؤثرة على جودة الصور المستقاة مثل تصحيح "غاما" وتردد اإلطار المأخوذ في االعتبار.
كما ناقشنا الخطوات التفصيلية لنظام حيازة الصور محل النقاش.
ايرة الصور وذلك لتقليل التشوه الناشئ عن العدسة تم كذلك عرض الخوارزميّة المقترحة لمع
المستخدمة. ولقد أثبتت الخوارزميّة المقترحة صّحتها وقدرتها على تصحيح العدسة والقيام بالتصحيح
.المنظوريّ
"سي –من منطق الحاالت الغامضة وطريقة "التعنقد بالمتوسط وقد وقع االختيار على كل
وذلك للقيام بعملية التوزيع والتجميع طبقا لأللوان، حيث يتم عمل عرض تمثيلي لعملية التوزيع والتجميع
هذه إلعطاء تفسير مرئي لعزل األشكال المرسومة بدال من استخدام عقد عنقدة زائفة.
91
" توتتّم عملية استخالص المالمح الهندسية بواسطة اكتشاف الحواّف باستخدام مرشّح "بريوي
الُمناَغم باألشعة الُمبَْرَمقِة ومطابقة الخطوط لتقييم أطوال الحواّف. وعالوة على ذلك، فإن الدرجات المقابلة
لألشكال الهندسيّة ودرجات األلوان يتم حسابها اعتمادا على القياسات المسجلة.
زفّي الخ بالط الحائطولقد تّم بناء مصنّف مؤسس على منطق الحاالت الغامضة لتمييز وفرز
له مدخالن )هما الشكل الهندسي ودرجة اللون( وثالث مخرجات )مقبول أو مبقّع لونيا أو المزخرف،
مخالف لونيا(.
وبناء نظام التقييم المقترح وتصنيعه. وقد أثبت اختبار وتقييم وتم كذلك تصميم منصة اختبار
الخزفّي المزخرف، واعدة بالط الحائط النظام أن نتائج استخدامه، من حيث دقة االكتشاف وتمييز ألوان
ومبشرة. وقد تمت أيضا دراسة إجراء التقييم باستخدام هذا النظام على بالطات خزفية غير ثابتة اتجاه
المحاور )ُمَمالة عن المحاور األصلية للنظام محل التطوير واالختبار( وذلك لعرض كفاءة هذا النظام
ير مثالية. وفي هذه األثناء، تم إجراء مقارنة لنتائج هذا النظام ونظام المعايرة المقترح تحت ظروف غ
رح.المقتحتى يتسنّى عمل تقييم شامل ألداء هذا النظام نتائج اختبار المقارنة المرجعّي المقترح مع