Statistical hypothesis testing and wavelet features for region segmentation

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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. © Springer-Verlag Berlin Heidelberg 2005.

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APA

Menoti, D., Borges, D. L., & De Albuquerque Araújo, A. (2005). Statistical hypothesis testing and wavelet features for region segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3773 LNCS, pp. 671–678). https://doi.org/10.1007/11578079_70

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