Statistical tuning of adaptive-weight depth map algorithm
Por:
Hoyos, Alejandro, Congote, John, Barandiaran, Inigo, Acosta, Diego, Ruiz, Oscar
Publicada:
1 ene 2011
Resumen:
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.
Filiaciones:
Hoyos, Alejandro:
EAFIT Univ, CAD CAM CAE LAB, Medellin, Colombia
Congote, John:
EAFIT Univ, CAD CAM CAE LAB, Medellin, Colombia
Ruiz, Oscar:
EAFIT Univ, CAD CAM CAE LAB, Medellin, Colombia
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