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

Chapter 1: Introduction

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 8: Regression models

Chapter 9: Classification models

Part III: Connecting local models

Chapter 10: Graphical models

Chapter 11: Models for chains and trees

Chapter 12: Models for grids

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

Chapter 16: Multiple cameras

Part VI: Models for vision

Chapter 17: Models for shape

Chapter 18: Models for style and identity

Chapter 19: Temporal models

Chapter 20: Models for visual words

Part VII: Appendices

Appendix A: Notation

Appendix B: Optimization

Appendix C: Linear algebra

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