Description: Probabilistic Graphical Models for Computer Vision., Hardcover by Ji, Qiang, ISBN 012803467X, ISBN-13 9780128034675, Like New Used, Free shipping in the US
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
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Book Title: Probabilistic Graphical Models for Computer Vision.
Number of Pages: Xv, 278 Pages
Publication Name: Probabilistic Graphical Models for Computer Vision
Language: English
Publisher: Elsevier Science & Technology
Publication Year: 2019
Subject: Engineering (General), Signals & Signal Processing, Data Processing
Type: Textbook
Author: Qiang Ji
Item Length: 9.2 in
Subject Area: Computers, Technology & Engineering
Item Width: 7.5 in
Format: Hardcover
