Jorge Rivera-Rovelo

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In this paper we present a method based on self-organizing neural networks to extract the shape of a 2D or 3D object using a set of transformations expressed as versors in the conformal geometric algebra framework. Such transformations, when applied to any geometric entity of this geometric algebra, define the shape of the object. This approach was tested(More)
This paper presents a method for segmentation of medical images and the application of the so called geometric or Clifford algebras for volume representation, non-rigid registration of volumes and object tracking. Segmentation is done combining texture and boundary information in a region growing strategy obtaining good results. To model2D surfaces and 3D(More)
The use of haptic interfaces in surgery could provide the surgeon useful sensing information about the patient tissues. Our goal in this work, is to use the haptic interface to obtain some sample points on the surface of an object or organ tissue in medical applications. This elasticity information feeds an artificial neural network. The output of the(More)