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Segmentation by texture using correlation.

P C Chen1, T Pavlidis

  • 1Exxon Production Research Company, Houston, TX 77001.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a texture segmentation method using correlation coefficients and a split-and-merge algorithm. It refines segmentation by grouping small regions based on gray levels for improved texture analysis.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Texture segmentation is crucial for image analysis.
  • Existing methods may struggle with uniform texture identification.
  • Hierarchical image representations offer potential for efficient segmentation.

Purpose of the Study:

  • To develop and evaluate a novel texture segmentation algorithm.
  • To utilize correlation coefficients for identifying uniform texture regions.
  • To improve segmentation accuracy through a hierarchical approach and post-processing.

Main Methods:

  • Correlation coefficients were evaluated on a quadratic picture tree (pyramid).
  • A split-and-merge algorithm was employed for preliminary segmentation based on texture uniformity.
  • A grouping algorithm, region adjacency graph, and small region elimination refined the segmentation.

Main Results:

  • The split-and-merge approach successfully identified and segmented regions based on texture similarity.
  • The grouping algorithm and small region elimination enhanced the final segmentation quality.
  • The method demonstrated effective texture segmentation on example images.

Conclusions:

  • The proposed method effectively segments images based on texture using correlation coefficients.
  • The hierarchical split-and-merge strategy combined with post-processing yields robust texture segmentation.
  • This approach offers a reliable way to analyze texture in image processing applications.