Computer Vision Models: Graphical models

Sunday, February 13, 2011

Graphical models

Since last time - finished chapter on graphical models, started work on shape chapter. Sorted out some embarrassing errors in the chapter on transformation models, added a number of algorithms to the section at the back and sorted out the section on linear algebra. Basic text is looking quite complete now, but still no references..

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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