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.
- Discusses PGM theories and techniques with computer vision examples
- Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision
- Includes an extensive list of references, online resources and a list of publicly available and commercial software
- Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction
Arvustused
"The book describes probabilistic graphical models in application to computer vision tasks. The theoretical concepts are accompanied by illustrative figures and algorithms in pseudocode. All the main categories of models are referred to. The applications range from image denoising and segmentation, object detection and tracking to 3D reconstruction and action recognition. It is a book that is valuable for theoreticians and practitioners alike." --zbMath/European Mathematical Society and the Heidelberg Academy of Sciences and Humanities
1. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision
Qiang Ji is in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, New York, USA
