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Robust rotation-invariant texture classification using a model based approach.

Patrizio Campisi1, Alessandro Neri, Gianpiero Panci

  • 1Dip. Elettronica Applicata, Universitá degli Studi di Roma Tre, 1-00146 Roma, Italy. campisi@uniroma3.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 15, 2005
PubMed
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This study presents a novel texture classification method using a model based on a linear system and binary images. Features from the spatial autocorrelation function (ACF) enable robust, rotation-invariant classification with a compact feature space.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Texture classification is crucial in image analysis.
  • Existing methods may lack robustness to rotation and noise.
  • Modeling texture via linear systems offers a new perspective.

Purpose of the Study:

  • To introduce a model-based texture classification procedure.
  • To demonstrate the efficacy of autocorrelation function (ACF) features.
  • To achieve rotation-invariant and noise-robust classification.

Main Methods:

  • Modeling texture as a linear system output driven by a binary image.
  • Extracting features from the spatial autocorrelation function (ACF) of the binary excitation.
  • Employing moment invariants for ACF classification.

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Main Results:

  • The proposed method achieves high correct rotation-invariant classification rates.
  • The approach is robust against additive noise.
  • The feature space size is effectively contained.

Conclusions:

  • Features from the ACF of the binary excitation are sufficient for texture representation and classification.
  • The moment invariants based technique ensures inherent rotation invariance.
  • This model-based approach offers an efficient and robust solution for texture classification.