nips nips2007 nips2007-81 nips2007-81-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Eric K. Tsang, Bertram E. Shi
Abstract: The peak location in a population of phase-tuned neurons has been shown to be a more reliable estimator for disparity than the peak location in a population of position-tuned neurons. Unfortunately, the disparity range covered by a phasetuned population is limited by phase wraparound. Thus, a single population cannot cover the large range of disparities encountered in natural scenes unless the scale of the receptive fields is chosen to be very large, which results in very low resolution depth estimates. Here we describe a biologically plausible measure of the confidence that the stimulus disparity is inside the range covered by a population of phase-tuned neurons. Based upon this confidence measure, we propose an algorithm for disparity estimation that uses many populations of high-resolution phase-tuned neurons that are biased to different disparity ranges via position shifts between the left and right eye receptive fields. The population with the highest confidence is used to estimate the stimulus disparity. We show that this algorithm outperforms a previously proposed coarse-to-fine algorithm for disparity estimation, which uses disparity estimates from coarse scales to select the populations used at finer scales and can effectively detect occlusions.
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