Parallel matching and reconstruction algorithms in computer vision

Abstract

Parallel implementations of a number of computer vision algorithms for visual reconstruction and matching are described. The algorithms chosen range in complexity from those requiring only image correlation and other simple operations to more sophisticated iterative algorithms involving relaxation, simulated annealing and graduated non-convexity. In each case the algorithms are implemented in parallel by mapping the images directly onto an SIMD processor array. Brief descriptions of the algorithms are given together with sample results and timings for their implementation on a 64 × 64 ICL DAP.