Computer Vision Models: April 2012

Tuesday, April 17, 2012

New version

Don't know if anyone still reads this, but I've posted a new version of the book on the main website today. A lot of small problems are fixed. The only major change is that I've made chapter 6 somewhat simpler by removing one of the examples. I've also posted the first half-decent version of the algorithms book and also updated all of the figures and powerpoint slides.

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