Probabilistic Graphical Models for Computer Vision

Released on by Qiang Ji
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  • Probabilistic Graphical Models for Computer Vision Book Detail

  • Author : Qiang Ji
  • Release Date : 2019-11
  • Publisher : Academic Press
  • Genre :
  • Pages : 294
  • ISBN 13 : 012803467X
  • File Size : 84,84 MB
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Probabilistic Graphical Models for Computer Vision by Qiang Ji PDF Summary

Book 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. 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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