Distributed image edge detection methods and performance

An edge detection process in computer vision and image processing detects any types of significant features appearing as discontinuities in intensities. This paper presents our experience with parallelizing an edge detection application algorithm that reduces noise and unnecessary detail in a gray-scale image from a coarse level to a fine level of resolution by using an edge focusing technique. Numerical methods and parallel implementations of edge focusing are presented. The image detection algorithms are implemented on three representative message-passing architectures: a low-cost heterogeneous PVM network, an Intel iPSC/860 hypercube, and a CM-5 massively parallel multicomputer. Our objectives are to provide insight into implementation and performance issues for image processing applications on general-purpose message-passing architectures, to investigate implications an network variations, and to evaluate the computing scalabilities on the three network systems by examining execution and communication patterns of the image edge detection application