| 书目名称 | Image Segmentation and Compression Using Hidden Markov Models | | 编辑 | Jia Li,Robert M. Gray | | 视频video | http://file.papertrans.cn/462/461460/461460.mp4 | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | In the current age of information technology, the issues ofdistributing and utilizing images efficiently and effectively are ofsubstantial concern. Solutions to many of the problems arising fromthese issues are provided by techniques of image processing, amongwhich segmentation and compression are topics of this book..Image segmentation is a process for dividing an image into itsconstituent parts. For block-based segmentation using statisticalclassification, an image is divided into blocks and a feature vectoris formed for each block by grouping statistics of its pixelintensities. Conventional block-based segmentation algorithms classifyeach block separately, assuming independence of feature vectors...Image Segmentation and Compression Using Hidden Markov Models.presents a new algorithm that models the statistical dependence amongimage blocks by two dimensional hidden Markov models (HMMs). Formulasfor estimating the model according to the maximum likelihood criterionare derived from the EM algorithm. To segment an image, optimalclasses are searched jointly for all the blocks by the maximum aposteriori (MAP) rule. The 2-D HMM is extended to multiresolution sothat more context inform | | 出版日期 | Book 2000 | | 关键词 | Information Technology (IT); Processing; Signal; algorithms; image processing; information; model; modeling | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-4497-5 | | isbn_softcover | 978-1-4613-7027-7 | | isbn_ebook | 978-1-4615-4497-5Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 2000 |
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