Del 571 - Springer International Series in Engineering and Computer Science

Image Segmentation and Compression Using Hidden Markov Models

Inbunden, Engelska, 2000

AvJia Li,Robert M. Gray

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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.

Produktinformation

Utgivningsdatum2000-08-31
Mått155 x 235 x 14 mm
Vikt412 g
FormatInbunden
SpråkEngelska
SerieSpringer International Series in Engineering and Computer Science
Antal sidor141
Upplaga2000
FörlagKluwer Academic Publishers
ISBN9780792378990

Tillhör följande kategorier

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.