Statistical hypothesis testing and wavelet features for region segmentation

Authors:
David Menoti;Díbio Leandro Borges;Arnaldo de Albuquerque Araújo
Affiliations:
Departamento de Ciência da Computação, UFMG – Universidade Federal de Minas Gerais, Grupo de Processamento Digital de Imagens, Belo Horizonte, MG, Brazil;BIOSOLO, Goiânia, Go, Brazil;Departamento de Ciência da Computação, UFMG – Universidade Federal de Minas Gerais, Grupo de Processamento Digital de Imagens, Belo Horizonte, MG, Brazil
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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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.