Computer Vision Models: Finally

Monday, July 19, 2010

Finally

I've been away from U of T for some time so I haven't updated the online copy until today. There have been a lot of changes in the mean time. I've finished the first full draft of the chapter on complex probability distributions (EM Algorithm, factor analysis, MoG and the like) and I'm now working on the chapters on regression and classification. I've also sorted out a whole bunch more mistakes from the first few chapters.

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Contents

Part I: Probability

1. Introduction to probability
2. Probability distributions
3. Fitting probability distributions
4. The multivariate normal

Part II: Machine learning for machine vision

5. Learning and inference
6. Complex probability densities
7. Regression models for vision
8. Classification models for vision

Part III: Connecting local models

9. Graphical models
10. Directed models for images
11. Markov random fields

Part IV: Preprocessing

12. Preprocessing methods

Part V: Models for geometry

13. Pinhole camera model
14. Transformation models
15. Multiple cameras

Part VI: Computer vision models

16. Models for shape
17. Models for identity and style
18. Temporal models
19. Models for visual words

Part VI Appendices

A. Optimization

B. Image preprocessing and feature extraction

C. Linear algebra

D. Algorithms