Overview

In an 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. Two of these, segmentation and compression, are discussed in this book. The authors present an 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. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization.

Full Product Details

Author:   Jia Li ,  Robert M. Gray
Publisher:   Springer
Imprint:   Springer
Edition:   2000 ed.
Volume:   571
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   0.900kg
ISBN:  

9780792378990

ISBN 10:   0792378997
Pages:   141
Publication Date:   31 August 2000
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD  
We will order this item for you from a manufatured on demand supplier.

Table of Contents

1. Introduction.- 1.1 Image Segmentation and Compression.- 1.2 Overview.- 2. Statistical Classification.- 2.1 Bayes Optimal Classification.- 2.2 Algorithms.- 2.3 Markov Random Fields.- 2.4 Markov Mesh.- 2.5 Multiresolution Image Classification.- 3. Vector Quantization.- 3.1 Introduction.- 3.2 Transform VQ.- 3.3 VQ as a Clustering Method.- 3.4 Bayes Vector Quantization.- 4 Two Dimensional Hidden Markov Model.- 4.1 Background.- 4.2 Viterbi Training.- 4.3 Previous Work on 2-D HMM.- 4.4 Outline of the Algorithm.- 4.5 Assumptions of 2-D HMM.- 4.6 Markovian Properties.- 4.7 Parameter Estimation.- 4.8 Computational Complexity.- 4.9 Variable-state Viterbi Algorithm.- 4.10 Intra- and Inter-block Features.- 4.11 Aerial Image Segmentation.- 4.12 Document Image Segmentation.- 5. 2-D Multiresolution Hmm.- 5.1 Basic Assumptions of 2-D MHMM.- 5.2 Related Work.- 5.3 The Algorithm.- 5.4 Fast Algorithms.- 5.5 Comparison of Complexity with 2-D HMM.- 5.6 Experiments.- 6. Testing Models.- 6.1 Hypothesis Testing.- 6.2 Test of Normality.- 6.3 Test of the Markovian Assumption.- 7. Joint Compression and Classification.- 7.1 Distortion Measure.- 7.2 Optimality Properties and the Algorithm.- 7.3 Initial Codebook.- 7.4 Optimal Encoding.- 7.5 Examples.- 7.6 Progressive Compression and Classification.- 8. Conclusions.- 8.1 Summary.- 8.2 Future Work.

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