CONTENIDO:Machine generated contents note: Part I. Probability: Introduction to probability
Common probability distributions
Fitting probability models
The normal distribution
PART II. MACHINE LEARNING FOR MACHINE VISION: LEARNING AND INFERENCE IN VISION
Modeling complex data densities
Regression models
Classification models
PART III. CONNECTING LOCAL MODELS: GRAPHICAL MODELS
Models for chains and trees
Models for grids
PART IV. PREPROCESSING: 1IMAGE PREPROCESSING AND FEATURE EXTRACTION
PART V. MODELS FOR GEOMETRY: 1THE PINHOLE CAMERA
Models for transformations
Multiple cameras
PART VI. MODELS FOR VISION: 1MODELS FOR STYLE AND IDENTITY
Temporal models
Models for visual words
PART VII. APPENDICES: A. OPTIMIZATION
B. Linear algebra
C. Algorithms