Examinando por Materia "Depth Map"

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    Ítem
    Real-time depth map generation architecture for 3D videoconferencing
    (2010-01-01) Congote, J.; Barandiaran, I.; Barandiaran, J.; Montserrat, T.; Quelen, J.; Ferran, C.; Mindan, P.J.; Mur, O.; Tarres, F.; Ruiz, O.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    In this paper we present a reliable depth estimation system which works in real-time with commodity hardware. The system is specially intended for 3D visualization using autostereoscopic displays. The core of this work is an implementation of a modified version of the adaptive support-weight algorithm that includes highly optimized algorithms for GPU, allowing accurate and stable depth map generation. Our approach overcomes typical problems of live depth estimation systems such as depth noise and flickering. Proposed approach is integrated within the versatile GStreamer multimedia software platform. Accurate depth map estimation together with real-time performance make proposed approach suitable for 3D videoconferencing. © 2010 IEEE.
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    Ítem
    Statiscal evaluation of the user-specified input parameters in an adaptive weight depth map algorithm
    (Universidad EAFIT, 2011) Hoyos Sierra, Alejandro; Ruiz Salguero, Oscar Eduardo
    In 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 structured 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 -- 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 significantly smaller data sets on fractional factorial and response surface design of experiments
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    Ítem
    Statistical tuning of adaptive-weight depth map algorithm
    (SPRINGER, 2011-01-01) Hoyos, Alejandro; Congote, John; Barandiaran, Inigo; Acosta, Diego; Ruiz, Oscar; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de Procesos
    In 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 significantly smaller data sets on fractional factorial and surface-response designs of experiments. © 2011 Springer-Verlag.
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    Ítem
    Tuning of adaptive weight depth map generation algorithms: Exploratory data analysis and design of computer experiments (DOCE)
    (SPRINGER, 2013-09-01) Acosta, Diego; Barandiaran, Inigo; Congote, John; Ruiz, Oscar; Hoyos, Alejandro; Grana, Manuel; Universidad EAFIT. Departamento de Ingeniería de Procesos; Desarrollo y Diseño de Procesos
    In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %. © 2012 Springer Science+Business Media, LLC.
  • No hay miniatura disponible
    Ítem
    Tuning of adaptive weight depth map generation algorithms: Exploratory data analysis and design of computer experiments (DOCE)
    (SPRINGER, 2013-09-01) Acosta, Diego; Barandiaran, Inigo; Congote, John; Ruiz, Oscar; Hoyos, Alejandro; Grana, Manuel; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAE
    In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %. © 2012 Springer Science+Business Media, LLC.