Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants.
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Table of Contents
1. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision
