Statiscal evaluation of the user-specified input parameters in an adaptive weight depth map algorithm

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 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