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A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification.

Lucas de Assis Soares1, Klaus Fabian Côco2, Patrick Marques Ciarelli2

  • 1Federal Institute of Espírito Santo, Linhares 29901-291, Brazil.

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|September 25, 2020
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Summary
This summary is machine-generated.

This study introduces a novel texture segmentation method using a convolutional neural network for pixel-wise classification. The approach accurately identifies texture borders without needing predefined classes, improving image analysis.

Keywords:
convolutional neural networkstexture analysistexture separation

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Area of Science:

  • Computer Vision
  • Image Analysis
  • Machine Learning

Background:

  • Texture segmentation is complex due to variations in scale, illumination, and subjective definitions.
  • Existing methods often require predefined texture classes, limiting flexibility.

Purpose of the Study:

  • To develop a texture segmentation method capable of binary pixel-wise classification without predefined classes.
  • To accurately identify internal texture regions and borders between different textures.

Main Methods:

  • Utilized a convolutional neural network with an encoder-decoder architecture for pixel classification.
  • Trained and tested the network on Prague Texture Segmentation Datagenerator and Benchmark, Brodatz, and Describable Texture Datasets.
  • Evaluated performance on remote sensing and H&E-stained tissue images.

Main Results:

  • Achieved good performance across diverse test sets, demonstrating precise border identification.
  • The method effectively segments textures without suffering from over-segmentation.
  • Successfully applied to specialized image types like remote sensing and medical tissue images.

Conclusions:

  • The proposed binary pixel-wise classification method offers a robust solution for texture segmentation.
  • It overcomes limitations of methods requiring predefined texture classes.
  • The approach shows significant potential for various image analysis applications.