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Boundary localization in texture segmentation.

S R Yhann1, T Y Young

  • 1Scripps Inst. of Oceanogr., California Univ., San Diego, La Jolla, CA.

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 multiresolution approach for accurate texture segmentation. The method effectively localizes boundaries while maintaining high labeling accuracy for image regions.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Accurate segmentation of textured image regions is challenging due to conflicting requirements for boundary localization and interior region labeling.
  • Traditional methods struggle with texture segmentation because texture features are spatially distributed, complicating boundary detection.

Purpose of the Study:

  • To develop a novel multiresolution approach for precise texture segmentation.
  • To address the inherent difficulties in localizing boundaries without compromising the labeling accuracy of image interiors.

Main Methods:

  • A multiresolution strategy combining localized and distributed texture features is proposed.
  • The approach leverages gray-level discontinuities at texture boundaries for precise localization.

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  • An algorithm is designed based on the characterized properties of gray-level discontinuities at texture boundaries.

    • Main Results:

    • The developed segmentation algorithm successfully identifies boundaries between textured regions.
    • The method demonstrates high performance on benchmark Brodatz texture mosaics.
    • Effective application is shown on real-world AVHRR satellite imagery.

    Conclusions:

    • The proposed multiresolution approach effectively solves the boundary localization problem in texture segmentation.
    • This method achieves maximum localization by utilizing gray-level discontinuities.
    • The algorithm proves robust and successful in diverse imaging applications.