Computer Vision Models: No longer ashamed

Friday, March 11, 2011

No longer ashamed

New update yesterday. I think we have reached the point, where I am no longer ashamed of any of the content in the book. Surely this is some kind of milestone! Almost finished the main content now - one more chapter to go. Since last time, I have re-written chapter 10 completely and also done some work on chapter 11, added some more algorithms to the back and and sorted out some mistakes in chapter 1-5.

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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