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Probabilistisc
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eBay-Artikelnr.:284374714879
Artikelmerkmale
- Artikelzustand
- Book Title
- Probabilistic Graphical Models for Computer Vision.
- Publication Date
- 2019-12-13
- ISBN
- 9780128034675
- Publication Year
- 2019
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Subject Area
- Computers, Technology & Engineering
- Publication Name
- Probabilistic Graphical Models for Computer Vision
- Publisher
- Elsevier Science & Technology
- Item Length
- 9.2 in
- Subject
- Engineering (General), Signals & Signal Processing, Data Processing
- Item Width
- 7.5 in
- Number of Pages
- Xv, 278 Pages
Über dieses Produkt
Product Identifiers
Publisher
Elsevier Science & Technology
ISBN-10
012803467X
ISBN-13
9780128034675
eBay Product ID (ePID)
18038260099
Product Key Features
Number of Pages
Xv, 278 Pages
Publication Name
Probabilistic Graphical Models for Computer Vision
Language
English
Publication Year
2019
Subject
Engineering (General), Signals & Signal Processing, Data Processing
Type
Textbook
Subject Area
Computers, Technology & Engineering
Format
Hardcover
Dimensions
Item Length
9.2 in
Item Width
7.5 in
Additional Product Features
Intended Audience
College Audience
LCCN
2020-277263
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
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.37
Table Of Content
1. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision
Synopsis
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, 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.
LC Classification Number
TA1634.J53 2020
Artikelbeschreibung des Verkäufers
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