Statistical Hypothesis Testing and Wavelet Features for Region Segmentation

  • David Menoti
  • Díbio Leandro Borges
  • Arnaldo de Albuquerque Araújo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

This paper introduces a novel approach for region segmentation. In order to represent the regions, we devise and test new features based on low and high frequency wavelet coefficients which allow to capture and judge regions using changes in brightness and texture. A fusion process through statistical hypothesis testing among regions is established in order to obtain the final segmentation. The proposed local features are extracted from image data driven by global statistical information. Preliminary experiments show that the approach can segment both texturized and regions cluttered with edges, demonstrating promising results. Hypothesis testing is shown to be effective in grouping even small patches in the process.

Keywords

Window Size Image Segmentation Input Image Output Channel Texturized Region 
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 2005

Authors and Affiliations

  • David Menoti
    • 1
  • Díbio Leandro Borges
    • 2
  • Arnaldo de Albuquerque Araújo
    • 1
  1. 1.Departamento de Ciência da ComputaçãoUFMG – Universidade Federal de Minas Gerais, Grupo de Processamento Digital de ImagensBelo HorizonteBrazil
  2. 2.BIOSOLOGoiâniaBrazil

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