| 书目名称 | Markov Random Field Modeling in Computer Vision | | 编辑 | S. Z. Li | | 视频video | | | 丛书名称 | Computer Science Workbench | | 图书封面 |  | | 描述 | Markov random field (MRF) modeling provides a basis for the characterization of contextual constraints on visual interpretation and enables us to develop optimal vision algorithms systematically based on sound principles. This book presents a comprehensive study on using MRFs to solve computer vision problems, covering the following parts essential to the subject: introduction to fundamental theories, formulations of various vision models in the MRF framework, MRF parameter estimation, and optimization algorithms. Various MRF vision models are presented in a unified form, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in the subject. | | 出版日期 | Book 19951st edition | | 关键词 | Markov Random Field; Markov random field theory; algorithms; computer vision; image processing; image res | | 版次 | 1 | | doi | https://doi.org/10.1007/978-4-431-66933-3 | | isbn_ebook | 978-4-431-66933-3Series ISSN 1431-1488 | | issn_series | 1431-1488 | | copyright | Springer Japan 1995 |
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发表于 2025-3-21 18:03:33
