Computing Stereopsis Using Feature Point Contour Matching

TLDR: A version of the Marr-Poggio-Grimson algorithm is presented that is characterized as matching extended contours of feature points obtained from a range of different spatial frequency tuned channels, and is illustrated on a series of natural images.

Abstract: Computational models of the human stereo system can provide insight into general information processing constraints that apply to any stereo system, either artificial or biological. In 1977, Marr and Poggio proposed one such computational model, that was characterized as matching certain feature points in difference-of-Gaussian filtered images, and using the information obtained by matching coarser resolution representations to restrict the search space for matching finer resolution representations. An implementation of the algorithm and its testing on a range of images was reported in 1980. Recent computational experiments applying the algorithm to a variety of natural images, especially aerial photographs, have led to a number of modifications. In this article, we present a version of the Marr-Poggio-Grimson algorithm that embodies these modifications, and is characterized as matching extended contours of feature points obtained from a range of different spatial frequency tuned channels. We illustrate its performance on a series of natural images.