| 書(shū)目名稱(chēng) | Markov Random Field Modeling in Image Analysis |
| 編輯 | Stan Z. Li |
| 視頻video | http://file.papertrans.cn/625/624645/624645.mp4 |
| 概述 | Comprehensive coverage over a broad range of Markov Random Field Theory.Provides the most recent advances in the field.Includes supplementary material: |
| 叢書(shū)名稱(chēng) | Advances in Computer Vision and Pattern Recognition |
| 圖書(shū)封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2009Latest edition |
| 關(guān)鍵詞 | Bayesian modeling; Bayesian network; Computer Vision; Computer vison; Markov random field; Optimization; S |
| 版次 | 3 |
| doi | https://doi.org/10.1007/978-1-84800-279-1 |
| isbn_softcover | 978-1-84996-767-9 |
| isbn_ebook | 978-1-84800-279-1Series ISSN 2191-6586 Series E-ISSN 2191-6594 |
| issn_series | 2191-6586 |
| copyright | Springer-Verlag London 2009 |
|Archiver|手機(jī)版|小黑屋|
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