Markov Random Field Modeling in Image Analysis

Li, Stan Z. (Chinese Academy of Sciences)

ISBN 10: 1848002785 / ISBN 13: 9781848002784

Edité par Springer London Ltd, 2009

This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 384 pages, 111 black & white illustrations, 11 black & white tables, biography. BIC Classification: PBT; UYQV. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 22. Weight in Grams: 713. . 2009. 3rd ed. 2009. Hardback. . . . . Books ship from the US and Ireland. N° de réf. du vendeur V9781848002784

Synopsis :

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Présentation de l'éditeur: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Détails bibliographiques

Titre : Markov Random Field Modeling in Image ...
Éditeur : Springer London Ltd
Date d'édition : 2009
Reliure : Couverture rigide
Etat : New
Edition : 3ème Édition