Image Textures and Gibbs Random Fields
Image analysis is one of the most challenging areas in today's computer sci ence, and image technologies are used in a host of applications. This book concentrates on image textures and presents novel techniques for their sim ulation, retrieval, and segmentation using specific Gibbs random fi...
Main Author: | |
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Format: | eBook |
Language: | English |
Published: |
Dordrecht
Springer Netherlands
1999, 1999
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Edition: | 1st ed. 1999 |
Series: | Computational Imaging and Vision
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Subjects: | |
Online Access: | |
Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Instead of introduction
- 1 Texture, Structure, and Pairwise Interactions
- 1.1 Human and computational views
- 1.2 Spatial homogeneity, or self-similarity of textures
- 1.3 Basic notation and notions
- 1.4 Random fields and probabilistic image modelling
- 1.5 Physics and image modelling: what an interaction means
- 1.6 GPDs and exponential families of distributions
- 1.7 Stochastic relaxation and stochastic approximation
- 2 Markov and Non-Markov Gibbs Image Models
- 2.1 Traditional Markov/Gibbs image models
- 2.2 Generalized Gibbs models of homogeneous textures
- 2.3 Prior Markov/Gibbs models of region maps
- 2.4 Piecewise-homogeneous textures
- 2.5 Basic features of the models
- 3 Supervised MLE-Based Parameter Learning
- 3.1 Affine independence of sample histograms
- 3.2 MLE of Gibbs potentials
- 3.3 Analytic first approximation of potentials
- 3.4 Most characteristic interaction structure
- 3.5 Stochastic approximation to refine potentials
- 4 Supervised Conditional MLE-Based Learning
- 4.1 The least upper bound condition
- 4.2 Potentials in analytic form
- 4.3 Practical consistency of the MLEs
- 5 Experiments in Simulating Natural Textures
- 5.1 Comparison of natural and simulated textures
- 5.2 “Brodatz” image database
- 5.3 Interaction maps and texture features
- 5.4 CSA vs. traditional modelling scenario
- 5.5 “MIT VisTex” image database
- 6 Experiments in Retrieving Natural Textures
- 6.1 Query-by-image texture retrieval
- 6.2 Similarity under scale and orientation variations
- 6.3 Matching two textures
- 6.4 Experiments with natural textures
- 6.5 Complexity and practicality
- 7 Experiments in Segmenting Natural Textures
- 7.1 Initial and final segmentation
- 7.2 Artificial collages of Brodatz textures
- 7.3 Natural piecewise-homogeneous images
- 7.4How to choose an interaction structure
- 7.5 Do Gibbs models learn what we expect?
- Texture Modelling: Theory vs. Heuristics
- References