Statistical tuning of adaptive-weight depth map algorithm

  • Authors:

  • Alejandro Hoyos;John Congote;Iñigo Barandiaran;Diego Acosta;Oscar Ruiz

  • Affiliations:

  • CAD CAM CAE Laboratory, EAFIT University, Medellin, Colombia;CAD CAM CAE Laboratory, EAFIT University, Medellin, Colombia and Vicomtech Research Center, Donostia-San Sebastián, Spain;Vicomtech Research Center, Donostia-San Sebastián, Spain;DDP Research Group, EAFIT University, Medellin, Colombia;CAD CAM CAE Laboratory, EAFIT University, Medellin, Colombia

  • Venue:
  • CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
  • Year:
  • 2011

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Abstract

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.