Probabilistic Graphical Models for Computer Vision.

Erscheinungsjahr 2017
ISBN: 978-0-12-803467-5
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 294 Seiten, Format (B × H): 243 mm x 198 mm, Gewicht: 742 g

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.

Autoren/Hrsg.

Ji, Qiang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning

Inhaltsverzeichnis

1. Introduction2. Probability Calculus3. Directed Probabilistic Graphical Models4. Undirected Probabilistic Graphical Models5. PGM Applications in Computer Vision

Über Autor(innen)

Ji, Qiang
Qiang Ji is in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, New York, USA