Abstract

Many low level visual computation problems such as focus, stereo, optical flow, etc. can be formulated as problems of extracting one or more parameters of a nonstationary transformation between two images. Because of the non-stationary nature, finite-width windows are widely used in various algorithms to extract spatially local information from images. While the choice of window width has a very profound impact on the quality of results of algorithms, there has been no quantitative way to measure or eliminate the negative effects of finite-width windows. To address this problem, We introduce two sets of filters, "moment" filters and "hypergeometric" filters. Due to their recursive properties, these filters allow the effects of finite-width windows and foreshortening to be explicitly analyzed and eliminated. We develop one paradigm to solve general one-parameter extraction problems using moment filters, and another one to solve general multiple parameter extraction problem using hyperge...

Citations