## Computer Vision: Models, Learning, and Inference (2012)

### Part I: Probability

**Chapter 2: Introduction to probability**

**Chapter 3: Common probability distributions**

**Chapter 4: Fitting probability models**

**Chapter 5: The normal distribution**

### Part II: Machine learning for machine vision

**Chapter 6: Learning and inference in vision**

**Chapter 7: Modeling complex data densities**

**Chapter 9: Classification models**

### Part III: Connecting local models

**Chapter 11: Models for chains and trees**

### Part IV: Preprocessing

**Chapter 13: Image preprocessing and feature extraction**

### Part V: Models for geometry

**Chapter 14: The pinhole camera**

**Chapter 15: Models for transformations
**

### Part VI: Models for vision

**Chapter 18: Models for style and identity**

**Chapter 20: Models for visual words**

### Part VII: Appendices

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