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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...

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Bibliographic Details
Main Author: Gimel'farb, Georgy L.
Format: eBook
Language:English
Published: Dordrecht Springer Netherlands 1999, 1999
Edition:1st ed. 1999
Series:Computational Imaging and Vision
Subjects:
Image Processing / Digital Techniques
Computer Vision
Artificial Intelligence
Data Structures And Information Theory
Probability Theory
Computer Imaging, Vision, Pattern Recognition And Graphics
Information Theory
Data Structures (computer Science)
Probabilities
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

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