ABSTRACT We developed a new approach based on Support Vector Machines (SVM) to model solids defined by a set of points on their surface. In problems of classification, regression and support of distribution, SVM are focused in the hyper-planes of maximum margin in the feature space. However, the forms that could be described by these surfaces when they return to the input space have not been studied in depth. In this paper, these surfaces are used to model complex objects, connected or non- connected, with a great mount of points, in order of tens of thousands, and with various topologies (hollow, branches, etc.). Two constrains https://www.viagrasansordonnancefr.com/viagra-en-ligne/ were kept: 1) The use of traditional algorithms of SVM theory; and 2) The design of the appropriate training sets from the object. This combination produced a novel tool and the results obtained illustrated the acheter cialis generique potential of the proposed method. Therefore, this new application of SVM of Vapnik is capable of creating surfaces of decision and estimation functions, which are well fitted to levitradosageus24.com objects of complex topology.
This paper proposes a new technique for 3D motion estimation of the left ventricle from a sequence of a heartbeat. Accurate motion estimation of the movement of cardiac walls has been shown to be very important for studying the cardiovascular illnesses. This technique is based on a processing chain from the acquisition to the 3D segmentation of the left ventricle area obtained from each image during the cardiac cycle. With the purpose of estimating the cialis generique movement of the Left Ventricle we calculate the optical flow starting from a sequence of images using the method proposed by Horn and Schunck . Our work demonstrates the applicability of Horn and Schunck algorithms for optical flow to estimate the 3D cardiac motion, and proposes to improve the https://www.acheterviagrafr24.com/vente-viagra/ accuracy of estimation by introducing constraints obtained by matching method.