Course Name: Machine Vision

 

Main Text book: Machine Vision

                            Authors: Ramesh Jain, Rangachar Kasturi and Brain G.Schunck

                            McGraw Hill International Editions, 1995

 

Supporting Text Books:

                           Digital Image Processing (2nd Edition)

                           Authors: Rafael C.Gonzalez, Richard E.Woods

                           Publisher: Prentice Hall, 2002

 

                           Machine Vision

                           Authors: Wesley E.Snyder and Hairong Qi

                           Cambridge University Press, 2004

                        

Course syllabus:

 

  1. Introduction

1.1    Machine Vision

1.2    Relationship to other fields

1.3    Image Geometry

1.4    Image Definition

1.5    Levels of Computation

 

  1. Binary Image Processing

2.1    Thresholding

2.2    Geometric Properties

2.3    Run-Length Encoding

2.4    Binary Algorithms

2.5    Morphological Operations on Binary Images

2.5.1        Erosion

2.5.2        Dilation

2.5.3        Opening and Closing

           2.7 Morphological Operations on Gray Scale Images

 

  1. Regions

3.1    Regions and Edge

3.2    Regions Representation

3.3    Split and Merge

3.4    Region Growing

3.5    Water-Shed Region Detection Algorithm

 

  1. Image Filtering

4.1    Linear Systems

4.2    Linear Filters

4.3    Gaussian Smoothing

4.3.1        Designing Gaussian Filters

  1. Edge Detection

5.1    Edge Detection Filters

5.2    Laplacian of Gaussian

5.3    Image Approximation

5.4    Gaussian Edge Detection

5.4.1        Canny Edge Detector

5.5    Sub-pixel Location Estimation

5.6    Edge Detection Performance

5.6.1        Methods for Evaluating Performance

5.6.2        Figure of Merit

 

  1. Contours

6.1    Digital Curves

6.1.1        Chain Codes

6.1.2        Slope Representation

6.2    Curve Fitting

6.3    Polyline Representation

6.4    Circular Arcs

6.5    Conic Sections

6.6    Curve Approximation

6.6.1        Total Regression

6.6.2        Estimating Corners

6.6.3        Robust Regression

6.6.4        Hough Transform

 

  1. Texture

7.1    Introduction

7.2    Statistical Methods of texture Analysis

7.3    Structural Analysis of Ordered Texture

7.4    Model Based Methods for Texture Analysis

 

  1. Depth

8.1    Stereo Imaging

8.2    Stereo Matching

8.2.1        Edge Matching

8.2.2        Region Correlation

8.3    Range Imaging

8.3.1        Structured Lighting

8.3.2        Imaging Radar

8.4    Active Vision

 

  1. Calibration

9.1    Coordinate System

9.2    Rigid Body Transformation

9.3    Absolute Orientation

9.4    Relative Orientation

9.5    Camera Calibration

  1. Dynamic Vision

10.1  Change Detection

10.2  Segmentation Using Motion

10.3  Image Flow

10.4  Segmentation using a Moving Camera

10.5  Tracking

10.5.1    Deviation Function for Path Coherence

10.5.2     Path Coherence Function

10.5.3     Path Coherence in Presence of Occlusion

10.5.4     Application in Control Traffic

10.5.5     Kalman Filter

 

  1. Object Recognition

11.1  Complexity of Object Recognition

11.2  Object Representation

11.3  Feature Detection

11.4  Recognition Strategies

11.5  Verification

         

 

Course Evaluation is done as follows:

1. Mid Term Exam

2. Final Exam

3. Seminar (Oral presentation of a research paper by students)

4. Course work (Implementation of some basic algorithms using MATHLAB)

5. Course Project (Programming a research topic that may be related to the seminar)