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Probabilistic Graphical Models for Computer Vision.

  • ID: 4772098
  • Book
  • December 2019
  • Elsevier Science and Technology

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.

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1. Introduction 2. Probability Calculus 3. Directed Probabilistic Graphical Models 4. Undirected Probabilistic Graphical Models 5. PGM Applications in Computer Vision

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Qiang Ji Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, New York, USA. Qiang Ji is in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, New York, USA
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