Graphical Models for Computer Vision

By: Qiang Ji

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Hardcover | 13 December 2019 | Edition Number 1

At a Glance

Hardcover
294 Pages

Dimensions(cm)
23.5 x 19.05 x 1.91

Hardcover

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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, and as well as using the PGM model to infer the unknown target variables given their image measurements.

Graphical Models for Computer Vision 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, accompanied by corresponding pseudocode for computer vision
Includes an extensive list of references, online resources, and a list of publically available and commercial software
Covers computer vision tasks such as feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking, and 3D reconstruction

Industry Reviews

"The book describes probabilistic graphical models in application to computer vision tasks. The theoretical concepts are accompanied by illustrative figures and algorithms in pseudocode. All the main categories of models are referred to. The applications range from image denoising and segmentation, object detection and tracking to 3D reconstruction and action recognition. It is a book that is valuable for theoreticians and practitioners alike." --zbMath/European Mathematical Society and the Heidelberg Academy of Sciences and Humanities

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