Computational Experiments with a Feature Based Stereo Algorithm
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 of 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. Since then a number psychophysical experiments have suggested possible refinements to the model and modifications to the algorithm. As well, 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 illustrate its performance on a series of natural images.