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Entropy-Scale Profiles for Texture Segmentation

  • Byung-Woo Hong
  • Kangyu Ni
  • Stefano Soatto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

Abstract

We propose a variational approach to unsupervised texture segmentation that depends on very few parameters and is robust to imaging conditions. First, the uneven illumination in the observed image is removed by the proposed image decomposition model that approximates the illumination and well retains the textures and features in the image. Then, from the obtained intrinsic image, we introduce a new data, multiscale local entropy, which is the entropy of each location’s neighborhood histogram with various scales. The proposed segmentation model uses multiscale local entropy as data. Together with a length penalizing term, minimizing the energy functional locates the contours so that the local entropy within each region is similar to one another. Since entropy is the only feature, there are very few parameters. Moreover, the segmentation model can be solved by a fast global minimization method. Experimental results on natural images show the proposed method is able to robustly segment various texture patterns with uneven illumination in the original images.

Keywords

Active Contour Texture Region Texture Pattern Texture Segmentation Local Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Byung-Woo Hong
    • 1
  • Kangyu Ni
    • 2
  • Stefano Soatto
    • 3
  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  2. 2.School of Mathematical and Statistical SciencesArizona State UniversityUSA
  3. 3.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

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