| 書目名稱 | Image Analysis, Random Fields and Dynamic Monte Carlo Methods | | 副標(biāo)題 | A Mathematical Intro | | 編輯 | Gerhard Winkler | | 視頻video | http://file.papertrans.cn/462/461408/461408.mp4 | | 叢書名稱 | Stochastic Modelling and Applied Probability | | 圖書封面 |  | | 描述 | This text is concerned with a probabilistic approach to image analysis as initiated by U. GRENANDER, D. and S. GEMAN, B.R. HUNT and many others, and developed and popularized by D. and S. GEMAN in a paper from 1984. It formally adopts the Bayesian paradigm and therefore is referred to as ‘Bayesian Image Analysis‘. There has been considerable and still growing interest in prior models and, in particular, in discrete Markov random field methods. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging. Among those are such ‘classical‘ applications like restoration, edge detection, texture discrimination, motion analysis and tomographic reconstruction. The subject is rapidly developing and in the near future is likely to deal with high-level applications like object recognition. Fascinating experiments by Y. CHOW, U. GRENANDER and D.M. KEENAN (1987), (1990) strongly support this belief. | | 出版日期 | Book 19951st edition | | 關(guān)鍵詞 | Markov Random Field; Monte Carlo; Monte Carlo method; Monte Carlos Methods; Probability theory; algorithm | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-97522-6 | | isbn_softcover | 978-3-642-97524-0 | | isbn_ebook | 978-3-642-97522-6Series ISSN 0172-4568 Series E-ISSN 2197-439X | | issn_series | 0172-4568 | | copyright | Springer-Verlag Berlin Heidelberg 1995 |
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