Probabilistic Graphical Models for Computer Vision
  • Release Date : 01 November 2019
  • Publisher : Academic Press
  • Genre : Uncategorized
  • Pages : 294 pages
  • ISBN 13 : 9780128034675
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Download or read book entitled Probabilistic Graphical Models for Computer Vision by author: Qiang Ji which was release on 01 November 2019 and published by Academic Press with total page 294 pages . This book available in PDF, EPUB and Kindle Format. 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

Semantic Multimedia

Semantic Multimedia

Author : Thierry Declerck,Michael Granitzer,Marcin Grzegorzek,Massimo Romanelli,Stefan Rüger,Michael Sintek
Publisher : Springer
Genre : Computers
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