Probabilistic Graphical Models for Computer Vision.
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Probabilistic Graphical Models for Computer Vision.

SKU: 9780128034958

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Additional information

Full Title

Probabilistic Graphical Models for Computer Vision.
Author(s)

Qiang Ji
Edition
ISBN

9780128034958, 9780128034675
Publisher

Academic Press
Format

PDF and EPUB

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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