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Maximum likelihood parameter estimation of textures using a Wold-decomposition based model.

J M Francos1, A Narasimhan, J W Woods

  • 1Dept. of Electr. and Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces a novel method for modeling and estimating parameters of natural textures using a Wold-like decomposition. The approach enables accurate analysis and synthesis of diverse texture types from image data.

Area of Science:

  • Image processing and computer vision
  • Statistical modeling of random fields
  • Signal processing

Background:

  • Modeling natural textures is complex due to their varied spectral distributions.
  • Existing methods often struggle with accurate parameter estimation for mixed spectral fields.

Purpose of the Study:

  • To develop a robust solution for modeling, parameter estimation, and synthesis of natural textures.
  • To address the challenge of analyzing texture fields with mixed spectral distributions.

Main Methods:

  • Utilizing a 2-D Wold-like decomposition to represent texture fields.
  • Implementing a two-stage maximum-likelihood algorithm for joint parameter estimation.
  • Transforming a nonlinear least-squares problem into a separable least-squares problem.

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Main Results:

  • Accurate estimation of harmonic and evanescent components within texture fields.
  • Successful refinement of spectral support parameters through iterative maximization.
  • A complete solution for field-model parameter estimation derived from transformed equations.

Conclusions:

  • The Wold-based model provides an effective framework for natural texture analysis and synthesis.
  • The proposed algorithm is applicable to a wide range of natural image textures.
  • This method offers a significant advancement in texture modeling and parameter estimation.