Disparity map computation on scalable computing

Abstract

This paper addresses the evaluation of a new disparity map computing algorithm characterized by a novel spurious removal strategy. Using this algorithm we eliminate a high percentage of wrong values with a low performance penalty. When testing images, incorrect percentages were reduced by 65% and 85%. This algorithm has been designed for scalable architectures with massive parallel processing elements. It works line by line with low memory requirements.