{"product_id":"image-segmentation-and-compression-using-hidden-markov-models-von-robert-m-gray-jia-li","title":"Image Segmentation and Compression Using Hidden Markov Models","description":"\n                                In the current age of information technology, the issues of  distributing and utilizing images efficiently and effectively are of  substantial concern. Solutions to many of the problems arising from  these issues are provided by techniques of image processing, among  which segmentation and compression are topics of this book.\n                \n                \u003cbr\u003e\n                                  Image segmentation is a process for dividing an image into its  constituent parts. For block-based segmentation using statistical  classification, an image is divided into blocks and a feature vector  is formed for each block by grouping statistics of its pixel  intensities. Conventional block-based segmentation algorithms classify  each block separately, assuming independence of feature vectors.\n                \n                \u003cbr\u003e\n                                  \n                \n                \u003cem\u003eImage Segmentation and Compression Using Hidden Markov Models\u003c\/em\u003e\n                                  presents a new algorithm that models the statistical dependence among  image blocks by two dimensional hidden Markov models (HMMs). Formulas  for estimating the model according to the maximum likelihood criterion  are derived from the EM algorithm. To segment an image, optimal  classes are searched jointly for all the blocks by the maximum a  posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so  that more context information is exploited in classification and fast  progressive segmentation schemes can be formed naturally.\n                \n                \u003cbr\u003e\n                                  The second issue addressed in the book is the design of joint  compression and classification systems using the 2-D HMM and vector  quantization. A classifier designed with the side goal of good  compression often outperforms one aimed solely at classification  because overfitting to training data is suppressed by vector  quantization.\n                \n                \u003cbr\u003e\n                                  \n                \n                \u003cem\u003eImage Segmentation and Compression Using Hidden Markov Models\u003c\/em\u003e\n                                 is  an essential reference source for researchers and engineers working in  statistical signal processing or image processing, especially those  who are interested in hidden Markov models. It is also of value to  those working on statistical modeling.\n            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9780792378990\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781461370277\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Hardcover - 9780792378990","offer_id":50726000134,"sku":"9780792378990","price":160.49,"currency_code":"EUR","in_stock":true},{"title":"Softcover - 9781461370277","offer_id":39415247143005,"sku":"9781461370277","price":160.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/364aad61-bd67-4e0f-9c0b-d197145dac7f.jpg?v=1774757559","url":"https:\/\/shop.autorenwelt.de\/products\/image-segmentation-and-compression-using-hidden-markov-models-von-robert-m-gray-jia-li","provider":"Autorenwelt Shop","version":"1.0","type":"link"}