Markov Random Field Modeling in Image Analysis:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer London
2009
|
| Ausgabe: | 3rd ed. 2009 |
| Schriftenreihe: | Advances in Computer Vision and Pattern Recognition
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-1-84800-279-1 https://doi.org/10.1007/978-1-84800-279-1 |
| Umfang: | 1 Online-Ressource (XXII, 362 Seiten 111 illus.) |
| ISBN: | 9781848002791 |
| ISSN: | 2191-6594 |
| DOI: | 10.1007/978-1-84800-279-1 |
Internformat
MARC
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| 505 | 8 | |a 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. 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. | |
| 505 | 8 | |a 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: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. | |
| 505 | 8 | |a Features: • 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 • 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 • 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 • 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 • Studies discontinuities, | |
| 505 | 8 | |a an important issue in the application of MRFs to image analysis • Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition • Includes an extensive list of references 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 | |
| 650 | 4 | |a Computer Vision | |
| 650 | 4 | |a Automated Pattern Recognition | |
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| 650 | 4 | |a Pattern recognition systems | |
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| 650 | 4 | |a Computer science / Mathematics | |
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Datensatz im Suchindex
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| author | Li, Stan Z. |
| author_facet | Li, Stan Z. |
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| author_variant | s z l sz szl |
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| contents | 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. 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. 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: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. Features: • 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 • 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 • 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 • 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 • Studies discontinuities, an important issue in the application of MRFs to image analysis • Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition • Includes an extensive list of references 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 |
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-84800-279-1 |
| edition | 3rd ed. 2009 |
| format | Electronic eBook |
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| isbn | 9781848002791 |
| issn | 2191-6594 |
| language | English |
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| spelling | Li, Stan Z. Verfasser aut Markov Random Field Modeling in Image Analysis by Stan Z. Li 3rd ed. 2009 London Springer London 2009 1 Online-Ressource (XXII, 362 Seiten 111 illus.) txt rdacontent c rdamedia cr rdacarrier Advances in Computer Vision and Pattern Recognition 2191-6594 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. 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. 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: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. Features: • 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 • 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 • 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 • 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 • Studies discontinuities, an important issue in the application of MRFs to image analysis • Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition • Includes an extensive list of references 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 Computer Vision Automated Pattern Recognition Probability Theory Mathematics of Computing Computer vision Pattern recognition systems Probabilities Computer science / Mathematics Erscheint auch als Druck-Ausgabe 9781848002784 Erscheint auch als Druck-Ausgabe 9781848826168 Erscheint auch als Druck-Ausgabe 9781849967679 https://doi.org/10.1007/978-1-84800-279-1 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Li, Stan Z. Markov Random Field Modeling in Image Analysis 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. 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. 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: Conditional Random Fields; Discriminative Random Fields; Total Variation (TV) Models; Spatio-temporal Models; MRF and Bayesian Network (Graphical Models); Belief Propagation; Graph Cuts; and Face Detection and Recognition. Features: • 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 • 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 • 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 • 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 • Studies discontinuities, an important issue in the application of MRFs to image analysis • Examines the problems of model parameter estimation and function optimization in the context of texture analysis and object recognition • Includes an extensive list of references 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 Computer Vision Automated Pattern Recognition Probability Theory Mathematics of Computing Computer vision Pattern recognition systems Probabilities Computer science / Mathematics |
| title | Markov Random Field Modeling in Image Analysis |
| title_auth | Markov Random Field Modeling in Image Analysis |
| title_exact_search | Markov Random Field Modeling in Image Analysis |
| title_full | Markov Random Field Modeling in Image Analysis by Stan Z. Li |
| title_fullStr | Markov Random Field Modeling in Image Analysis by Stan Z. Li |
| title_full_unstemmed | Markov Random Field Modeling in Image Analysis by Stan Z. Li |
| title_short | Markov Random Field Modeling in Image Analysis |
| title_sort | markov random field modeling in image analysis |
| topic | Computer Vision Automated Pattern Recognition Probability Theory Mathematics of Computing Computer vision Pattern recognition systems Probabilities Computer science / Mathematics |
| topic_facet | Computer Vision Automated Pattern Recognition Probability Theory Mathematics of Computing Computer vision Pattern recognition systems Probabilities Computer science / Mathematics |
| url | https://doi.org/10.1007/978-1-84800-279-1 |
| work_keys_str_mv | AT listanz markovrandomfieldmodelinginimageanalysis |