Probabilistic Graphical Models for Computer Vision.

Helps readers solve their computer vision problems using Probabilistic Graphical Models

Qiang Ji (Author)

9780128034675, Elsevier Science

Hardback, published 13 December 2019

294 pages
23.4 x 19 x 2.3 cm, 0.77 kg

"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

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. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision

Subject Areas: Communications engineering / telecommunications [TJK]