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 (DRF), - Strong Random Fields (SRF), - Spatial-Temporal Models, - Learning MRF for Classification (motivation + DRF). Comprehensive coverage over a broad range of Markov Random Field Theory Provides the most recent advances in the field INDICE: Introduction.- Mathematical MRF Models.- Low Level MRF Models.- High Level MRF Models.- Discontinuities in MRFs.- Discontinuity-Adaptivity Modeland Robust Estimation.- MRF Parameter Estimation.- Parameter Estimation in Optimal Object Recognition.- Minimization: Local Methods.- Minimization: Global Methods.- List of Notation.- Index.
- ISBN: 978-1-84800-278-4
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 385
- Fecha Publicación: 01/11/2008
- Nº Volúmenes: 1
- Idioma: Inglés