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We introduce a nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing. The approach considers the problem as a variational one, which consists of minimizing a weighted sum of two energy terms: a regularization one that uses a discrete weighted -Dirichlet energy and an approximation one.(More)
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution(More)
Pattern Recognition ()-Keywords: Cytological and histological images Pathology Weighted graphs Image processing tools Discrete regularization Fast image processing Automatic and interactive segmentation schemes We propose a framework of graph-based tools for the segmentation of microscopic cellular images. This framework is based on an object oriented(More)
In this paper, we describe a new scheme to color image segmentation which is based on supervised pixel classification methods. Using color pixel classification alone does not extract accurately enough color regions, so we suggest to use a strategy based on four steps in different color spaces: simplification, pixel classification, marker extraction and(More)
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(More)
The processing of color images has become a major field of interest, however the direct extension of their gray scale counterparts is not always possible since there is no natural ordering of color vectors. Mathematical morphology has to face with this problem since it needs a complete lattice which is generally based on a conditional ordering. We propose(More)
This paper presents a color object recognition scheme which proceeds in three sequential steps: segmentation, features extraction and classification. We mainly focus on the first and the third steps here. A color watershed using global and local criteria is first described. A color contrast value is defined to select the best color space for segmenting(More)
We propose a nonlinear multiscale decomposition of signals defined on the vertex set of a general weighted graph. This decomposition is inspired by the hierarchical multiscale (BV,L 2) decomposition of Tadmor, Nezzar, and Vese (Multiscale Model. Simul. 2(4):554–579, 2004). We find the decomposition by iterative regularization using a graph variant of the(More)
Partial difference equations (PDEs) and variational methods for image processing on Euclidean domains spaces are very well established because they permit to solve a large range of real computer vision problems. With the recent advent of many 3D sensors, there is a growing interest in transposing and solving PDEs on surfaces and point clouds. In this paper,(More)
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution(More)