Markov Random Field Modeling in Image Analysis (Advances in Computer Vision and Pattern Recognition) - Hardcover

Li

Hardcover

ISBN 10: 1848002785 ISBN 13: 9781848002784

Verlag: Springer, 2009

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.

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<p>Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles.</p><p></p><p>This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimization. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution.</p><p></p><p>Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as:&nbsp;Conditional Random Fields; Discriminative Random Fields;&nbsp;Total Variation (TV) Models;&nbsp;Spatio-temporal Models;&nbsp;MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts;&nbsp;and Face Detection and Recognition.&nbsp;</p><p>Features:</p><p>• Focuses on applying Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain</p><p>• Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice, and MRFs on relational graphs derived from images</p><p>• Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation</p><p>• Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting</p><p>• Studies discontinuities, an important issue in the application of MRFs to image analysis</p><p>• Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition</p><p>• Includes an extensive list of references</p><p></p><p>This broad-ranging and comprehensive volume 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 relating to these areas.</p>

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2009
• Sprache: Englisch
• ISBN 10: 1848002785
• ISBN 13: 9781848002784
• Einband: Tapa dura
• Auflage: 3
• Anzahl der Seiten: 384
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Deutschland