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Rough Fuzzy Computing for Unsupervised Image Segmentation

By Michele Ceccarelli and Alfredo Petrosino


In this paper we consider the problem of unsupervised boundary localization in textured images, reporting a texture separation algorithm which extracts textural density gradients by a non-linear multiple scale-space analysis of the image. Texture boundaries are extracted by segmenting the images resulting from a multiscale fuzzy gradient operation applied to detail images. The segmentation stage consists of a parallel hierarchical clustering algorithm, aimed at the minimization of a global cost functional taking into account region homogeneity and segmentation quality. Experiments on Brodatz textures and real images are reported. 1

Year: 2014
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