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