Graphical Models for Computer Vision
Auteur : Ji Qiang
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
1. Introduction 2. Probability Calculus 3. Directed Probabilistic Graphical Models 4. Undirected Probabilistic Graphical Models 5. PGM Applications in Computer Vision
Date de parution : 11-2019
Ouvrage de 500 p.
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