Statistical Hypothesis Testing and Wavelet Features for Region Segmentation
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.
KeywordsWindow Size Image Segmentation Input Image Output Channel Texturized Region
- 3.Haralick, R., Shapiro, L.: Computer and Robot Machine Vision. Addison-Wesley, USA (1992 and 1993)Google Scholar
- 5.Galun, M., Sharon, E., Basri, R., Brandt, A.: Texture segmentation by multiscale aggregation of filter responses and shape elements. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2003), Nice, France, pp. 716–723 (2003)Google Scholar
- 8.Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes in C, 2nd edn. Cambridge University Press, UK (1996)Google Scholar