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.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of Figures - p. vii 6. List of Tables - p. xv 7. 1: Background and motivation - p. 1 8. 2: Foundation and basics - p. 11 9. 3: Directed probabilistic graphical models - p. 31 10. 4: Undirected probabilistic graphical models - p. 151 11. 5: Computer vision applications - p. 191 12. Index - p. 299
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.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of Figures - p. vii 6. List of Tables - p. xv 7. 1: Background and motivation - p. 1 8. 2: Foundation and basics - p. 11 9. 3: Directed probabilistic graphical models - p. 31 10. 4: Undirected probabilistic graphical models - p. 151 11. 5: Computer vision applications - p. 191 12. Index - p. 299
