machine vision machine vision system components ent 273 ms. hema c.r. lecture 1
TRANSCRIPT
MACHINE VISIONMACHINE VISION
Machine Vision System ComponentsMachine Vision System Components
ENT 273ENT 273
Ms. HEMA C.R.Ms. HEMA C.R.
Lecture 1.
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Road Map
Image and Vision
Vision Systems
Components of an Machine Vision System [MVS]
Applications of vision systems
Advantages of MVS
Vision Optics
Frame Grabbers
Lighting and Illumination
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Image and Vision Image
Images are two-dimensional projections of the three-dimensional world
Vision Vision is the most Complex of human senses, about a fourth of the
brain’s volume is devoted to it. Image Processing
Processing images to give new images Computer Vision
Deals with what the images mean – aims to interpret images Machine Vision
Apply vision and image processing Vision System
A Vision System recovers useful information about a scene from its two dimensional projections
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Machine Vision Systems
Characteristics Ability to extract pertinent information
from a background of irrelevant details The capacity to learn from examples
and apply to new situations Ability to infer facts from incomplete
information Capability to generate self motivated
goals and formulate plans for meeting these goals.
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Components of a Machine Vision System
Input source objects, scene, prints etc
Optics sensors, digital cameras
Lighting illumination levels
A part sensor [optional] to indicate presence of objects
A frame grabber stores images & interface
PC platform [optional] Inspection software
Image processing algorithms Digital I/O
Display, Print, Interface
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Vision System Portrayal
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Operations to be performed by MVS
Describe images, objects and physical world Mathematical models of image and objects and knowledge
representation Image Processing
Improves image for human and computer consumption, highlight / extract relevant feature
Segmentation Extract features such a edge, regions, surfaces etc.
Pattern Recognition Classify the images
Measurement Analysis Measure features on the object
Image Understanding Locate objects in the image, classify them and build 3D models
The Ultimate Aim of a Vision System is to recognize objects within a image
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Applications of a Vision System
Autonomous Vehicles The Human Face Industrial Inspection Medical Images Remote SensingSurveillanceTransport
Reference: http://www.bmva.ac.uk/apps/
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Autonomous Vehicles
Aerial Navigation
Transport Safety
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The Human Face
Head Modeling
Face Recognition
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Industrial Inspection
Detecting Objects
Machine parts
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Medical Images
Chromosomes
Brain MRI
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Remote Sensing
Crop Classification
Land Management
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Surveillance
Intruder Monitoring
People Tracking
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Transport
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Number Plate
Traffic Control
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Advantages of MVS in Industries
Cutting out defective goods
Making better use of raw materials
Cutting the cost of quality control
Enabling real-time process monitoring
Improving employment conditions
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Vision Systems Stand alone PC based
Smart Camera Self contained [no pc req.]
CCD image sensors CMOS image sensors
Vision Sensors Integrated devices No programming required Between smart cams and vision systems
Digital Cameras CCD image CMOS image Flash memory Memory stick SmartMedia cards Removable [microdrives,CD,DVD]
Vision Optics
Neural Network-Based ZiCAMs from JAI Pulnix
Compact Vision System from National Instruments
A Cognex In-Sight Vision Sensor
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Imaging Sensors Image sensors convert light into electric charge and
process it into electronic signals
Image Sensors Charge Coupled Device CCD
All pixels are devoted to light capture Output is uniform High image quality Used in cell phone cameras
Complementary Metal Oxide Semiconductor CMOS Pixels devoted to light capture are limited Output is not uniform High Image quality Used in professional and industrial cameras
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Frame Grabbers
A frame grabber is a device to acquire [grab] and convert analog to digital images. Modern FG have many additional features like more storage, multiple camera links etc.
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Frame Grabbers
A typical frame grabber consists of
a circuit to recover the horizontal and vertical synchronization pulses from the input signal;
An analog to digital converter a colour decoder circuit, a function that can also
be implemented in software some memory for storing the acquired image
(frame buffer) a bus interface through which the main processor
can control the acquisition and access the data.
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Lighting Correct lighting is the single most important design
parameter in a vision system
Selection of a light source for a vision application is governed by three factors:
The type of features that must be captured by the vision system
The need for the part to be either moving or stationary when the image is captured.
The degree of visibility of the environment in which the image is captured.
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Lighting Techniques
The three lighting techniques used in vision applications are:
Front lighting, Back lighting Structured lighting
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Front Lighting Sources
Spot Lighting to check chip
orientation in embossed tape
Ring Shape Lighting to detect
loose caps
Tube Lighting to detect stains on
sheets
Area type lighting to detect hole position
in lead frames
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Visithttp://www.machinevisiononline.org
http://www.eeng.dcu.e/~whelanp/proverbs/proverbs.pdf
to understand vision systems better
Interesting Links
References:http://www.bmva.ac.uk/apps/www.machinevisiononline.orghttp://homepages.inf.ed.ac.uk/rbf/CVonline
Machine Vision Machine Vision
End of Lecture 1