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 us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for sol...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Tokyo
Springer Japan
2001
|
| Ausgabe: | 2nd ed. 2001 |
| Schriftenreihe: | Computer Science Workbench
|
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | 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. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. 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 second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. 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 |
| Beschreibung: | 1 Online-Ressource (XIX, 323 p) |
| ISBN: | 9784431670445 |
| DOI: | 10.1007/978-4-431-67044-5 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804182062367768576 |
|---|---|
| adam_txt | |
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| any_adam_object_boolean | |
| author | Li, Stan Z. |
| author_facet | Li, Stan Z. |
| author_role | aut |
| author_sort | Li, Stan Z. |
| author_variant | s z l sz szl |
| building | Verbundindex |
| bvnumber | BV047064399 |
| classification_rvk | SK 820 ST 330 |
| collection | ZDB-2-SCS |
| ctrlnum | (ZDB-2-SCS)978-4-431-67044-5 (OCoLC)1227484058 (DE-599)BVBBV047064399 |
| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4 |
| dewey-search | 006.4 |
| dewey-sort | 16.4 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| doi_str_mv | 10.1007/978-4-431-67044-5 |
| edition | 2nd ed. 2001 |
| format | Electronic eBook |
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| id | DE-604.BV047064399 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:12:22Z |
| indexdate | 2024-07-10T09:01:34Z |
| institution | BVB |
| isbn | 9784431670445 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032471511 |
| oclc_num | 1227484058 |
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| owner_facet | DE-706 |
| physical | 1 Online-Ressource (XIX, 323 p) |
| psigel | ZDB-2-SCS ZDB-2-SCS_2000/2004 ZDB-2-SCS ZDB-2-SCS_2000/2004 |
| publishDate | 2001 |
| publishDateSearch | 2001 |
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| publisher | Springer Japan |
| record_format | marc |
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| spelling | Li, Stan Z. Verfasser aut Markov Random Field Modeling in Image Analysis by Stan Z. Li 2nd ed. 2001 Tokyo Springer Japan 2001 1 Online-Ressource (XIX, 323 p) txt rdacontent c rdamedia cr rdacarrier Computer Science Workbench 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. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. 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 second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. 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 Pattern Recognition Image Processing and Computer Vision Mathematics of Computing Pattern recognition Optical data processing Computer science—Mathematics Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Markov-Zufallsfeld (DE-588)4168933-1 gnd rswk-swf Parameterschätzung (DE-588)4044614-1 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s Parameterschätzung (DE-588)4044614-1 s Markov-Zufallsfeld (DE-588)4168933-1 s Maschinelles Sehen (DE-588)4129594-8 s DE-604 Erscheint auch als Druck-Ausgabe 9784431670452 Erscheint auch als Druck-Ausgabe 9784431703099 https://doi.org/10.1007/978-4-431-67044-5 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Li, Stan Z. Markov Random Field Modeling in Image Analysis Pattern Recognition Image Processing and Computer Vision Mathematics of Computing Pattern recognition Optical data processing Computer science—Mathematics Maschinelles Sehen (DE-588)4129594-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Parameterschätzung (DE-588)4044614-1 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4006684-8 (DE-588)4168933-1 (DE-588)4044614-1 |
| 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_exact_search_txtP | 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 | Pattern Recognition Image Processing and Computer Vision Mathematics of Computing Pattern recognition Optical data processing Computer science—Mathematics Maschinelles Sehen (DE-588)4129594-8 gnd Bildverarbeitung (DE-588)4006684-8 gnd Markov-Zufallsfeld (DE-588)4168933-1 gnd Parameterschätzung (DE-588)4044614-1 gnd |
| topic_facet | Pattern Recognition Image Processing and Computer Vision Mathematics of Computing Pattern recognition Optical data processing Computer science—Mathematics Maschinelles Sehen Bildverarbeitung Markov-Zufallsfeld Parameterschätzung |
| url | https://doi.org/10.1007/978-4-431-67044-5 |
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