Examinando por Materia "Pixels"

Mostrando 1 - 4 de 4
  • No hay miniatura disponible
    Ítem
    Spatial layout and surface reconstruction from omnidirectional images
    (Institute of Electrical and Electronics Engineers Inc., 2016-01-01) Posada, L.F.; Velasquez-Lopez, A.; Posada, L.F.; Velasquez-Lopez, A.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)
    This paper presents a spatial layout recovery approach from single omnidirectional images. Vertical structures in the scene are extracted via classification from heterogeneous features computed at the superpixel level. Vertical surfaces are further classified according to their main orientation by fusing oriented line features, floor-wall boundary features and histogram of oriented gradients (HOG) with a Random Forest classifier. Oriented line features are used to build an orientation map that considers the main vanishing points. The floor-wall boundary feature attempts to reconstruct the scene shape as if it were observed from a bird's-eye view. Finally, the HOG descriptors are aggregated per superpixel and summarize the gradient distribution at homogeneous appearance regions. Compared to existing methods in the literature which rely only on corners or lines, our method gains statistical support from multiple cues aggregated per superpixel which provide more robustness against noise, occlusion, and clutter. © 2016 IEEE.
  • No hay miniatura disponible
    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.
  • 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 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.