Probabilistic Graphical Models for Computer Vision by Qiang Ji

Probabilistic Graphical Models for Computer Vision by Qiang Ji

By: Qiang Ji

Publisher: Academic Press

Print ISBN: 9780128034675, 012803467X

eText ISBN: 9780128034958, 0128034955

Copyright year: 2020

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