Probabilistic Graphical Models for Computer Vision is popular PDF and ePub book, written by Qiang Ji in 2019-11, it is a fantastic choice for those who relish reading online the Uncategoriezed genre. Let's immerse ourselves in this engaging Uncategoriezed book by exploring the summary and details provided below. Remember, Probabilistic Graphical Models for Computer Vision can be Read Online from any device for your convenience.
Probabilistic Graphical Models for Computer Vision 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
Detail Book of Probabilistic Graphical Models for Computer Vision PDF
- Author : Qiang Ji
- Release : 01 November 2019
- Publisher : Academic Press
- ISBN : 9780128034675
- Genre : Uncategoriezed
- Total Page : 294 pages
- Language : English
- PDF File Size : 9,6 Mb
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