
Probabilistic Graphical Models for Computer Vision.
Qiang Ji(Author)
Academic Press
Published on 13. December 2019
Book
Hardback
294 pages
978-0-12-803467-5 (ISBN)
Description
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.
Reviews / Votes
"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 HumanitiesMore details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Product notice
Laminated cover
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
765 gr
ISBN-13
978-0-12-803467-5 (9780128034675)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
12/2019
Academic Press
€79.95
Available for download
Person
Qiang Ji is in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, New York, USA
Author
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, New York, USA
Content
1. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision