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In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R³. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).<strong></strong>

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Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.

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