Wavelet-Based Correlation for Stereopsis
نویسنده
چکیده
Abstract. Position disparity between two stereoscopic images, combined with camera calibration information, allow depth recovery. The measurement of position disparity is known to be ambiguous when the scene reflectance displays repetitive patterns. This problem is reduced if one analyzes scale disparity, as in shape from texture, which relies on the deformations of repetitive patterns to recover scene geometry from a single view. These observations lead us to introduce a new correlation measure based not only on position disparity, but on position and scale disparity. Local scale disparity is expressed as a change in the scale of wavelet coefficients. Our work is related to the spatial frequency disparity analysis of Jones and Malik (ECCV92). We introduce a new wavelet-based correlation measure, and we show its application to stereopsis. We demonstrate its ability to reproduce perceptual results which no other method of our knowledge had accounted for.
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