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Examinando Capítulos en libros por Autor "Barandiaran, Iñigo"
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Ítem Evaluation of interest point detectors for image information extraction(2012) Barandiaran, Iñigo; Goenetxea, Jon; Congote, John; Graña, Manuel; Ruíz, Oscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEInterest points extraction and matching is a common task in many computer vision based application, which are used in different domains, such as 3D reconstruction, object recognition, or tracking -- We present an evaluation of current state of the art about interest point extraction algorithms to measure several parameters, such as detection quality, invariance to rotation and scale transformation, and computational efficiencyÍtem Extending Marching Cubes with Adaptative Methods to obtain more accurate iso-surfaces(Springer Berlin Heidelberg, 2010) Congote, John; Moreno, Aitor; Barandiaran, Iñigo; Barandiaran, Javier; Ruíz, Oscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEThis work proposes an extension of the Marching Cubes algorithm, where the goal is to represent implicit functions with higher accuracy using the same grid size -- The proposed algorithm displaces the vertices of the cubes iteratively until the stop condition is achieved -- After each iteration, the difference between the implicit and the explicit representations is reduced, and when the algorithm finishes, the implicit surface representation using the modified cubical grid is more accurate, as the results shall confirm -- The proposed algorithm corrects some topological problems that may appear in the discretization process using the original gridÍtem Statistical tuning of Adaptive-Weight Depth Map Algorithm(Springer Berlin Heidelberg, 2011) Hoyos, Alejandro; Congote, John; Barandiaran, Iñigo; Acosta, Diego; Ruíz, Óscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEIn depth map generation, the settings of the algorithm parameters to yield an accurate disparity estimation are usually chosen empirically or based on unplanned experiments -- A systematic statistical approach including classical and exploratory data analyses on over 14000 images to measure the relative influence of the parameters allows their tuning based on the number of bad pixels -- Our approach is systematic in the sense that the heuristics used for parameter tuning are supported by formal statistical methods -- The implemented methodology improves the performance of dense depth map algorithms -- As a result of the statistical based tuning, the algorithm improves from 16.78% to 14.48% bad pixels rising 7 spots as per the Middlebury Stereo Evaluation Ranking Table -- The performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury -- Future work aims to achieve the tuning by using signicantly smaller data sets on fractional factorial and surface-response designs of experiments