Computer Vision Models: Since yesterday

Tuesday, October 12, 2010

Since yesterday

Worked on preprocessing chapter some more - wrote first draft of section on textons and local binary patterns. Corrected a bunch of mistakes in Chapters 5 and 8. Improved a figure in chapter 5. Added a couple of questions to chapter 3 and one for chapter 4. Added another algorithm to the recipes section. Phew.

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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