Machine Vision 1392.10.
The aim of course:
To give sufficient theoretical depth on important topics on
Machine vision and using the OpenCV programming
developing environment to implement programming
assignments and course project.
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After graduating this course, it is expected that the students
have enough theoretical and practical knowledge to start
industrial applications of Machine Vision.
Machine Vision
Text Books:
1. Computer Vision
Linda G.Shapiro, George C.Stockman
Prentice Hall, 2001
2. Digital Image Processing (3rd Edition)
Rafael C.Gonzalez, Richard E.Woods
Prentice Hall, 2008
3. Learning OpenCV, Computer Vision with the
OpenCV Library
Gary Bradski and Adrian Kaebler,
O’REILLY, 2008
Course Syllabus
Chapter-1
Introduction,
What is Machine Vision
Its applications
Relation of Machine Vision with related
fields such as
Image Processing,
Computer Graphics and
Artificial Intelligence
Chapter-2
Digital Image Fundamentals
Elements of visual perception
Structure of the Human eye
Image formation in the Eye
Brightness adaption and discrimination
Light and Electromagnetic spectrum
Chapter-2
Digital Image Fundamentals
Image sensing and acquisition
Image acquisition using a single sensor
Image acquisition using sensor strips
Image acquisition using sensor arrays
A simple image formation model
Image sampling and quantization
Basic concepts in sampling and quantization
Representing digital images
Spatial and intensity resolution
Image interpolation
Chapter-2
Digital Image Fundamentals
Some basic relationships between pixels
Neighbors of pixels
Adjacency, Connectivity, Regions, and Boundary
Distance measures
An introduction to mathematical tools used in
digital image processing
Array versus matrix operations
Linear versus non-linear operations
Arithmetic operations
Chapter-3
Binary Image Analysis
Pixels and neighborhoods
Applying masks to images
Counting the objects in an image
Connected component labeling
Binary Image Morphology
Structuring element
Basic morphological operations
Dilation and Erosion
Opening and Closing
The Hit-or-Miss transformation
Chapter-3
Binary Image Analysis
Some basic morphological algorithms
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Boundary extraction
Hole filling
Extraction of connected components
Convex Hull
Thinning
Thickening
Skeleton
Pruning
Chapter-3
Binary Image Analysis
Region Properties
Region adjacency graphs
Thresholding gray-scale images (some
basic methods)
4. Image Segmentation
Fundamentals
Point, Line, and edge detection
Background
Detection of isolated points
Line detection
Edge models
Basic edge detection
More advanced techniques for edge detection
Edge linking and boundary detection
4. Image Segmentation
Thresholding
Foundation
Basic Global thresholding
Optimum global thresholding and Otsu’s
Method
Multiple thresholding
Variable thresholding
Multivariable thresholding
Thresholding in un-even illumination
4. Image Segmentation
Region-based segmentation
Region growing
Region splitting and merging
Segmentation using morphological
watersheds
The use of motion in segmentation
5.
Color and Shading
Color Fundamentals
Color Models
The RGB color model
The CMY and CMYK color model
The HIS, YIQ and YUV color models
Pseudo color image processing
Basics of full color image processing
5.
Color and Shading
Color Transformation
Color slicing
Tone and color corrections
Color histograms
Image segmentation based on color
Noise in color images
5.
Color and Shading
Shading
Radiation from one light source
Diffuse reflection
Specular reflection
Darkening with distance
Phong model of shading
5.
Color and Shading
Color constancy
The color of objects taken under different
lighting conditions (lights different from
white) look different from their real color.
How can we convert these colors to their
real colors as if the image was taken under
normal white color.
6.
Texture
Texture, Texels (Texture element) and Statistics
Texture descriptions
Quantitative (statistical) texture measures
- Co-occurrence matrices
- Laws texture energy measures
- Tamura texture measure
* In computer graphics, textures are represented by array of texles.
6.
Texture
Texture segmentation
Structural approaches
Spectral approaches
7. Content based image retrieval
Image database examples
Database organization
- Standard indexing
- Spatial indexing
- Indexing for content based image retrieval with multiple measures
Image database Queries
Image distance measures
- Color, Texture and Shape similarity measures
Precision and Recall measures to evaluate the performance
of a CBIR system
8. Representation and description
Representation
Boundary following
Chain codes
Signatures
Boundary descriptors
Regional descriptors
Use of Principal components for description
An example for object recognition
9. Motion from 2D image sequences
Motion phenomena and applications
Image subtraction
Computing motion vectors
Using point correspondences
MPEG compression of video
Computing image flow
Computing the path of moving points
Detecting significant changes in video
10. Perceiving 3D from 2D images
Intrinsic images
Labeling of line drawing from blocks world
3D cues available in 2D images
The perspective imaging model
Depth perception from stereo
Estimating correspondences using cross correlation
Correspondences using epipolar constrains
10. Perceiving 3D from 2D images
The thin lens equation
Lens distortion
11. Tracking
Different vision systems used for motion
detection
Reference image
A control traffic application
Corner finding
Invariant Features
SIFT (Scale Invariant Feature Transform)
11. Tracking
Mean-Shift segmentation and Tracking
Cam-shift tracking
Kalman Filter
Particle filters
Introducing tracking systems based on machine
learning approaches
12. Camera model and calibration
Intrinsic camera parameters
Extrinsic camera parameters
A calibration example
13. Omni-directional mirrors and vision
system
Applications of Omni-Vision system
Design parameters for omni-mirrors
Camera calibration in omni-vision
Omni-vision application in soccer robots
14. Introducing some industrial applications of
machine vision in Iran
1. How to detect defected eggs
2. Selecting the best stones for asphalt
Course Evaluation
1. Mid term exam-1 (2.5)
2. Mid term exam-2 (2.5)
2. Final Exam
(5)
3. Seminar (Oral presentation of a research paper or a
book chapter by students) ( 2 )
4. Course work (Implementation Machine Vision
algorithms using OpenCV) ( 6 )
5. Course Project (Programming a research topics
that may be related to the seminar) ( 2 )