Probabilistic Graphical Models for Computer Vision

This PDF book is become immediate popular in Computers genre. Probabilistic Graphical Models for Computer Vision is written by famous author Qiang Ji and Ready to Download in ePUB, PDF or Kindle formats. Released by Academic Press in 2019-12-12. Click Download Book button to get book file and read directly from your devices. Here is a quick description and cover image of Probabilistic Graphical Models for Computer Vision book.

Probabilistic Graphical Models for Computer Vision
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Publisher : Academic Press
Release Date :
ISBN : 9780128034958
Pages : 294 pages
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Probabilistic Graphical Models for Computer Vision by Qiang Ji Book PDF Summary

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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Probabilistic Graphical Models for Computer Vision
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Probabilistic Graphical Models for Computer Vision PDF book is popular Computers book written by Qiang Ji. The book was released by Academic Press in 12 December 2019 with total hardcover pages 294. 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

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