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A noise-aware coding scheme for texture classification.

Mohammad Shoyaib1, M Abdullah-Al-Wadud, Oksam Chae

  • 1Department of Computer Engineering, Kyung Hee University, Yongin 446-701, Korea. shoyaib@khu.ac.kr

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a noise-tolerant ternary pattern (NTTP) for robust image texture analysis. NTTP effectively distinguishes true textures from noise, outperforming existing methods on benchmark datasets.

Keywords:
adaptive noise bandnoise tolerant ternary patterntexlet

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Texture analysis is crucial in computer vision and image processing.
  • Existing methods for codifying texture micro-patterns (texlets) often fail with noisy real-world images.
  • Noise can create false textures, hindering reliable image representation.

Purpose of the Study:

  • To develop a noise-robust method for texture analysis.
  • To accurately represent texlets in images despite noise contamination.
  • To improve the reliability of texture descriptors in practical applications.

Main Methods:

  • An adaptive noise band (ANB) is defined to estimate noise levels around pixels.
  • Noise-tolerant ternary patterns (NTTP) are generated using the ANB for texlet representation.
  • The proposed method is evaluated on benchmark texture datasets (Outex, Brodatz).

Main Results:

  • NTTP demonstrates superior performance compared to state-of-the-art methods.
  • The method effectively differentiates between true textures and noise-induced artifacts.
  • Reliable texlet representation is achieved even in the presence of various noise types.

Conclusions:

  • The proposed NTTP method offers a significant advancement in noise-robust texture analysis.
  • NTTP provides a reliable way to represent image texlets, overcoming limitations of existing techniques.
  • This approach enhances the applicability of texture analysis in real-world scenarios with noisy images.