Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Markov Random Field Modeling in Image Analysis (Computer Science Workbench)

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S. Z. Li 作者
Springer-Verlag Telos
譯者
2001-07 出版日期
323 頁數
USD 59.95 價格
Paperback
叢書系列
9784431703099 圖書編碼

Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 圖書標籤: 我也是  小布的信息科學  MRF   


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Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 著者簡介


Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 著者簡介


Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Markov Random Field Modeling in Image Analysis (Computer Science Workbench) 在線電子書 圖書描述

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. 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.

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