ECE 515 - Image Analysis and Computer Vision II

Description: Credit 4 hours. Image Analysis techniques, 2D & 3D Shape representation, Segmentation, Camera and Stereo
modeling, Motion, Generic object and face recognition, parallel and Neural Architectures for image and visual processing. 

Prerequisite: ECE 415  or consent of the instructor.

Recent Textbooks:

1. Digital Image Processing book by R.C. Gonzalez and R.E. Woods, Prentice Hall 2001.

2. Course notes given to students (about 100 pages) and IEEE Transactions papers. 


 1. Survey of Image Analysis Techniques 
      a. Feature Detection and Extraction 
      b. Feature Matching 
      c. Edge Detection 
      d. Canny’s Edge Detector 
      e. Ramp and Edge Detection by Expansion Matching 
      f. Generalized Feature Detection

 2. 2-D and 3-D Shape Representation 
      a. Viewer Centered Representation 
      b. Object Centered Representation

 3. Image Segmentation and Feature Grouping 
      a. Hough Transform for Feature Detection 
      b. Gestalt Principles of Grouping

 4. Shape Analysis and Segmentation 

 5. Introduction to Neural Networks for Pattern Recognition and Image Processing 

 6. Biological Vision Systems 
      a. Their description with neural networks 
      b. Human-perceptual organization

 7. Camera Parameters and Estimation of Orthographic and Perspective Imaging Projections 

 8. Stereo and Motion Imaging for 3-D Information Recovery 
      a. Motion Flow Fields 
      b. Shape Form Methods

 9.  Scale Space Techniques for Describing Images with Varying Detail 
      a. Pyramidal Architectures

 10. AI Methods for Vision, Search Methods, Probabilistic Models for Vision 

 11. Template Matching for Generic Object and Face Recognition

 12. 2-D and 3-D Object Recognition 
      a. Geometric Hashing 
      b. Multidimensional Indexing 
      c. Affine Invariant Iconic Recognition

 13. Practical Vision Systems 
      a. Algorithms and Architectures 
      b. Neural Architectures