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Image Segmentation and Compression Using Hidden Markov Models

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 a...

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Bibliographic Details
Main Authors: Jia Li, Gray, Robert M. (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 2000, 2000
Edition:1st ed. 2000
Series:The Springer International Series in Engineering and Computer Science
Subjects:
Computer Graphics
Computer Vision
Electrical And Electronic Engineering
Electronic Data Processing / Management
Electrical Engineering
Signal, Speech And Image Processing
Signal Processing
It Operations
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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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