Titlebook: Image Segmentation and Compression Using Hidden Markov Models; Jia Li,Robert M. Gray Book 2000 Springer Science+Business Media New York 20

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Introduction, Environmental scientists use aerial and satellite imagery to study pollution patterns. For entertainment, television bringing live pictures to our homes is a part of modern life. Classical images, drawings and paintings, have been giving human beings the enjoyment of art since the dawn of civilization.

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Joint Compression and Classification,eads to improved compression and classification jointly. We call this algorithm the HMM VQ algorithm. Comparisons are made with BVQ, a joint compression and classification algorithm developed by Oehler, Gray, Perlmutter, Olshen, ., which is described in Section 3.4.

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Vector Quantization, contains 176 × 144 pixels. Each pixel is described by one luminance component and two chrominance components, each stored originally in 1 byte. Sin the chrominance components are subsampled every 2× 2 pixels, one image requires 176× 144 × 1.5 bytes, i.e., 304128 bits, to specify. For a video sequen

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Two Dimensional Hidden Markov Model,ch as selecting block sizes dynamically ac cording to context. The improvement achieved demonstrates the potential of context to help classification. In this chapter, a two dimensional hidden Markov model (2-D HMM) is introduced as a general framework for context dependent classifiers.

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Image Segmentation and Compression Using Hidden Markov Models

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Jia Li,Robert M. Grayw in spring 2005, by the wide array of new independently designed car models and concept cars on display from Chinese firms such as Chery, Geely, and First Auto Works. In terms of sales, Chery had shocked both foreign and domestic observers when it announced that in 2003, it had sold 90,387 cars, mo

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