A novel method for color image segmentation is proposed in this paper. The method is based on the segmentation of each color plane independently using a watershed based thresholding of the plane histograms. The segmentation maps obtained for each color plane are fused together according to a fusion operator taking into account a concordance of the labels of each segmentation map. This operator ...

In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results....

We present an approach for hypothesis-based image segmentation founding on the integration of level set methods and discriminative feature clustering techniques. Building up on previous work, we investigate Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) to train a classifier for foreand background of an image. Here we extend this concept towards level set segmentation algori...

This paper deals with the clearance of images by the Fusion technique. The main objective of image fusion is to extract all the useful information from the source images. It does not introduce artifacts or inconsistencies which will distract human observers or any processing. Image fusion is reliable and robust to imperfections such as misregistration. Fusion is a useful technique in image proc...

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achi...