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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Long-correlation image models for textures with circular and elliptical correlation structures.

K B Eom1

  • 1Department of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USA. com@scas.gwu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
Summary
This summary is machine-generated.

A new random field model offers isotropic or elliptical long-correlation structures for texture modeling. This efficient model uses few parameters to capture intuitive texture attributes, enabling realistic image synthesis.

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Area of Science:

  • * Image processing and texture analysis.
  • * Computational statistics and random field modeling.

Background:

  • * Existing long-correlation models lack isotropic or elliptical correlation structures.
  • * Diverse real-world textures require flexible and parameter-efficient modeling approaches.

Purpose of the Study:

  • * Introduce a novel class of random field models with isotropic/elliptical long-correlation characteristics.
  • * Develop a model with few parameters that intuitively represent texture attributes.
  • * Validate the model's efficacy in synthesizing realistic textures.

Main Methods:

  • * Fractional differencing of a 2-D autoregressive polynomial with eight symmetric neighbors.
  • * Development of a three-step parameter estimation algorithm.
  • * Statistical analysis of parameter estimator properties.

Main Results:

  • * The model effectively captures texture attributes like smoothness, pattern size, and orientation.
  • * Synthesized images from estimated parameters closely resemble real textures.
  • * The model demonstrates versatility in modeling a wide range of textures.

Conclusions:

  • * The proposed random field model provides a flexible and efficient framework for texture modeling.
  • * The intuitive parameterization facilitates control over synthesized texture characteristics.
  • * The model shows significant potential for applications in image synthesis and analysis.