Complex Correlation Statistic for Dense Stereoscopic Matching

Abstract

A traditional solution of area-based stereo uses some kind of windowed pixel intensity correlation. This approach suffers from discretization artifacts which corrupt the correlation value. We introduce a new correlation statistic, which is completely invariant to image sampling, moreover it naturally provides a position of the correlation maximum between pixels. Hereby we can obtain sub-pixel disparity directly from sampling invariant and highly discriminable measurements without any postprocessing of the discrete disparity map. The key idea behind is to represent the image point neighbourhood in a different way, as a response to a bank of Gabor filters. The images are convolved with the filter bank and the complex correlation statistic (CCS) is evaluated from the responses without iterations.

Evaluation of Stereo Matching Costs on Images with Radiometric Differences

“…This method is often used for pixel-wise global methods, but can also be used for window-based matching. (Other window-based sampling-insensitive costs exist [40] but are not evaluated here. )…”