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Title: Regularizing a set of unstructured 3D points from a sequence of stereo images
Authors: Alvarez-Leon, L 
Cuenca Hernández, Carmelo 
Sánchez, Javier 
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
120601 Construcción de algoritmos
120602 Ecuaciones diferenciales
120326 Simulación
Keywords: Fields
Issue Date: 2003
Journal: Lecture Notes in Computer Science 
Conference: 4th International Conference on Scale Space Methods in Computer Vision 
Abstract: In this paper we present a method for the regularization of a set of unstructured 3D points obtained from a sequence of stereo images. This method takes into account the information supplied by the disparity maps computed between pairs of images to constraint the regularization of the set of 3D points. We propose a model based on an energy which is composed of two terms: an attachment term that minimizes the distance from 3D points to the projective lines of camera points, and a second term that allows for the regularization of the set of 3D points by preserving discontinuities presented on the disparity maps. We embed this energy in a 2D finite element method. After minimizing, this method results in a large system of equations that can be optimized for fast computations. We derive an efficient implicit numerical scheme which reduces the number of calculations and memory allocations.
ISBN: 3-540-40368-X
ISSN: 0302-9743
Source: Griffin L.D., Lillholm M. (eds) Scale Space Methods in Computer Vision. Scale-Space 2003. Lecture Notes in Computer Science, vol 2695. Springer, Berlin, Heidelberg
Rights: by-nc-nd
Appears in Collections:Artículos
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