digital image processing fundamentals - vidya-mitra...
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Digital Image Processing
and Machine Vision
Fundamentals
By Dr. Rajeev Srivastava Associate Professor
Dept. of Computer Sc. & Engineering, IIT(BHU), Varanasi
Overview
• In early days of computing, data was numerical.
• Later, textual data became more common.
• Today, many other forms of data such as voice, music, speech, images, computer graphics, etc.
• Each of these types of data are signals.
• Loosely defined, a signal is a function that
conveys information.
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Relationship of Signal Processing
to other fields
• As long as people have tried to send or receive through electronic media : telegraphs, radar, telephones, television etc there has been the realization that these signals may be affected by the system used to acquire, transmit, or process them.
• Sometimes, these systems are imperfect and introduce noise, distortion, or other artifacts.
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• Understanding the effects these systems have and finding ways to correct them is the fundamental of signal processing.
• Sometimes, these signals are specific messages that we create and send to someone else (e.g., telegraph, telephone, television, digital networking, etc.).
• That is, we specifically introduce the information content into the signal and hope to extract it out later.
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• Sometimes, these man-made signals are encoding of natural phenomena (audio signal, acquired image, etc.),but sometimes we can create them from scratch (speech generation, computer generated music, computer graphics).
• Finally, we can sometimes merge these technologies together by acquiring a natural signal, processing it, and then transmitting it in some fashion.
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Concerned fields of Signal
Processing
• Digital Communication
• Compression
• Speech Synthesis and Recognition
• Computer Graphics
• Image Processing
• Computer Vision
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What is an image?
• A representation, likeness, or imitation of an object or thing
• A vivid or graphic description
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Why do we need images?
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• Various imaging modalities help us to see invisible objects due to
-Opaqueness (e.g., see through human body) -Far distance (e.g., remote sensing) -Small size (e.g., light microscopy)
• Other signals (e.g., seismic) can also be translated into images to facilitate the analysis • Images are important to convey information and support reasoning
A picture is worth a thousand words!
Fields related to images
• Computer Graphics
• Image Processing
• Computer Vision
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Input /Output Image Description
Image
Image Processing
Computer Vision
Description
Computer Graphics
AI
Computer Graphics
Computer Graphics deals with generation of 2D computer images from the descriptions of real time 3D object or data.
Computer graphics deals with designing suitable 2D scene images to simulate our 3D world.
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Image Processing
Image processing is the manipulation of an image for the purpose of either extracting information from the image or producing an alternative representation of the image.
Image processing is a subclass of signal processing concerned specifically with pictures.
Improve image quality for human perception and/or computer interpretation.
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Computer Vision
Computer vision is related to the construction of the 3D world from the observed 2D images.
Computer vision deals with the analysis of image content.
Computer graphics pursues the opposite direction in designing suitable 2D scene images to simulate our 3D world.
Image processing can be considered as the crucial middle way connecting the vision and graphics fields.
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Computer Vision
• Computer Vision components:
Image Processing
Image Analysis
Image Understanding
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Process Input Output
Image Processing Image Analysis Image Understanding
Image Image Image
Image Measurements High-level description
Related Fields
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Signal Processing
Physics
Imaging
NeuroBiology Mathematics
Machine Learning
Artificial Intelligence
Control Robotics
Computer Vision
Image Processing
Machine Vision
DIGITAL IMAGE
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Satellite image
Volcano Kamchatka Peninsula, Russia
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Satellite image
Volcano in Alaska
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Medical Images:
MRI of normal brain
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Medical Images:
X-ray knee
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Ultrasound: Five-month Foetus
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Astronomical images
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Radiation from the Electromagnetic spectrum
Acoustic
Ultrasonic
Electronic (in the form of electron beams used in electron microscopy)
Computer (synthetic images used for modelling and visualization)
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Categorization by Image
Sources
Radiation from EM Spectrum
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Electromagnetic Spectrum
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X-ray Imaging
Imaging in Ultraviolet Band
Imaging in Visible and
Infrared Bands
Imaging in Microwave Band
Imaging in Radio Band
Other Imaging Modalities that uses neither of energy bands from EM radiation
Acoustic Imaging :Use of sound waves to capture images of an object.
Examples: Geographical Explorations, Ultrasound Imaging.
Computer generated images
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Acoustic Imaging
Ultrasound Imaging
Generated Images by
Computer
Application
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Image Acquisition
• An image is captured by a sensor such as
a monochrome or color TV camera and
digitized.
• If the output of the camera or sensor is
not already in digital form, an analog-to-digital converter digitizes it.
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Digital Image Acquisition Process (Ref: R.C. Gonzalez)
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FROM ANALOG TO DIGITAL
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Record
output Display
Object Observe
Imaging
systems
Digitize
Sample and
quantize
Store
Digital
storage
(disk)
Process
Digital
computer
Refresh
/store
On-line
buffer
Digital Image Representation
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•Images are 2D signals represented in 2D matrix form.
•Each element of matrix is known as pixels.
•Each pixel is associated with some integer/real value that represents intensity or color at that point.
Types of images
Binary Images
Gray scale images
Indexed color images
RGB color images
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Binary Image
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Each Pixel have either 0 or 1 value. 0white 1black
Intensity (Gray-Level) Image
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•Each pixel is associated with 8-bits of Intensity. •A pixel may attain any value in between 0 to 255.
Coloured Image
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Indexed Color Images
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Typically 256 colors (GIF-format)
RGB Color Images
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Red, green and blue channels, typically 256 levels each: 2(3*8) = 16777216 colors. (e.g. TIF and JPEG formats)
General Purpose Image Processing
System
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Computer
Mass Storage
Image Processing Software
Image Sensors
Specialized Image
Processing Hardware
Hardcopy
Image Displays
Problem Domain
Hardware Required
Camera
Hardware Required
Frame Grabber
Image Processing
• Manipulation of multidimensional signals
image (photo)
video
CT, MRI
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),,,( tzyxf
Image Processing
Why it is needed?
For:
– Coding/compression
– Enhancement, restoration, reconstruction
– Analysis, detection, recognition, understanding
– Visualization
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Image Processing Tasks
Enhancement
Restoration
Edge Detection
Segmentation
Compression
Object Description etc.
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Image Enhancement
Another example : MRI Power Law Transformation: CRγ (Ref: R.C. Gonzalez)
i. A magnetic resonance image of an upper thoracic human spine with a fracture dislocation and spinal cord impingement – The picture is predominately dark – An expansion of gray levels are
desirable needs < 1 ii. Result after power-law transformation with = 0.6, c=1 iii. Transformation with = 0.4 (best result) iv. Transformation with = 0.3 (under acceptable level)
Image Restoration
Distorted Image Restored Image
Types of Distortion in Images: Distortion due to Camera Misfocus
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Original image Distorted image
Distortion due to motion
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Camera lens
Distortion due to Random
Noise
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Distorted image Original image
Types of noises in an image
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Applications: Image
Inpainting
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Image Inpainting
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Applications: Reduction of Speckle Noise From Ultrasound Images
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Applications: Reduction of Speckle Noise From Remote Sensing SAR Images
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Applications: Restoration and Enhancement of Microscopic Images
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Image Segmentation
Image Segmentation
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Applications of Segmentation
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Where do we require it ?
Extraction of the desired object/constituent from an image
For volumetric analysis in MRI images for early detection of diseases like Alzheimer’s, Parkinson’s disease and Schizophrenia.
Motion tracking : e.g. finding the speed of an aeroplane.
Detection or identification of objects in an image in the field of Robotics
Automatic car assembly in robotic vision
Various Techniques used
Edge Detection
Active Contour Snake model
Registration and Masking in images
Color Image Processing
Compression
Image Compression
Signal-Processing Based:
Encoder
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H ),( yxg Compressed
Representation
Decoder
1H
),(ˆ yxf),( yxg
Morphological Processing
Representation and
Description
Representation and
Description
Recognition and Description
Knowledge Base
Ex: Postal Code Problem
Not all the processes are needed
Computer Vision System:
Framework
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Image Preproce
-ssing
Input Image
Feature Extraction
Segmentn
Feature Extraction
Symbolic Reprstn
Interpretation and
description
Classification and
description
Image Analysis System
Image Understanding System
Data Analysis Conclusion from Analysis
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Application Areas of
Computer Vision
• Controlling Processes : An industrial robot or an autonomous vehicle
• Detecting Events: For Visual Surveillance or people counting
• Organizing Information: For indexing databases of images and image sequences
• Modeling objects or environments: Industrial Inspection ( Detection in circuit of PCB layout) and Medical Image
Analysis • Interaction : As the input to a device for computer human
interaction • 3D- shape modeling
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Digital Image processing and Machine Vision: Applications
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RADAR imaging
Gray level image
processing
Medical Imaging
Biometrics (Forensic science)
Remote sensing
Copy right protection-
Digital water marking
Digital holography
Image Compression
DIP and Machine
Vision
And many more: object recognition, 3D vision, Robotics, Industrial Inspection etc.
Areas of applications
Other application Areas of Image
Processing and Vision
Image Registration
Optical Coherence Tomography
Remote Sensing and Agriculture
Astronomy
Digital Watermarking
Microscopic image processing of biological samples
Scientific Visualization
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Applications: Medical Image Registration Initial Condition of MR-PET Registration
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Final Configuration for MR-PET Registration
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Applications of Registration
• Register 2 MRIs of brain to visualize anatomy and tumor
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Applications of Registration…
• Create at 3-D model for surgical planning and visualization
Tumor(green), Vessels(red), Ventricles(blue), Edema (orange)
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Segmentation Applications in Medical Imaging: Semi-Automated Detection of
Alzheimer’s Disease using segmentation (Active-Contour Model)
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Coronal view
Axial view
Saggital view
Framework for snakes
• A higher level process or a user initializes any curve close to the
object boundary.
• The snake then starts deforming and moving towards the
desired object boundary.
• In the end it completely “shrink-wraps” around the object.
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ALZHEIMER’S DISEASE (AD)
Most Common cause of Dementia
Characterized by progressive cognitive deterioration
Can be diagnosed accurately only after Biopsy
Latest research : AD leads to atrophy of Hippocamus and Corpus Callosum of the Brain
Corpus Callosum
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SEGMENTED HIPPOCAMPUS
HIPPOCAMPUS IN CORONAL SLICE 8/3/2014 92
SEGMENTED CORPUS CALLOSUM
CORPUS CALLOSUM IN SAGGITAL SLICE
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Digital Watermarking
Host Image (Top Left), Watermark (Top right), Visible Watermarked image (Bottom Left), and Invisible watermark (Bottom Right)
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Applications of Computer Vision in Space
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Remote Sensing and Vision in Agriculture
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Remote Sensing and Vision in Agriculture
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References:
[BOOK]Digital Image Processing By R.C. Gonzalez