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X-ray angiography has been an effective modality for diagnosing coronary artery disease representing one of the leading causes of death. A 3D reconstruction of the coronary arteries is a very important step to facilitate the interpretation of angiograms. In this paper, we propose a 3D reconstruction method of the coronary arteries. In order to improve the pairwise matching performance of our approach, we introduce artificial interest points (nodes) to respect the topology variation between 3D vascular trees. New similarity measures are proposed to take into consideration the non-rigid coronary artery movement. To measure the performance of the proposed method, we evaluate the proposed method on clinical data. Results show that accuracy of vessel centerlines has the average projection error equal to 0.5 mm for 28 different patients.

Here, we intend to introduce new face invariant descriptors, composed by two kinds of features, in order to explore the problem of faces classification. The first kind is defined from the p-order moments of a curvature function of the geodesic curve according to its arc length. The second one describes relative positions between important localities of faces. Two classes Fisher discriminate analysis is applied for a dimension reduction. A two dimensional multi classes Expectation Maximization algorithm (2D-EM) is used to identify the components of the mixture distribution. Then, the classification is obtained after applying the Bayes decision rule which is the most optimal for the minimization of the classification error. Such classification gives the sub groups having homogenous similar faces.