X. Zhang, S. G. Dykes, and H. Deng 
Distributed edge detection: issues and implementations

IEEE Computational Science & Engineering, Spring Issue, April 1997, pp. 72-82. 
 
Abstract
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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 {\it edge focusing} technique.
Numerical methods and parallel implementations
of edge focusing are presented.
The image detection algorithms are implemented on three representative
massage-passing architectures: a low-cost heterogeneous PVM network,
an Intel iPSC/860 hypercube, and a CM-5
massively parallel multicomputer.
The CM-5 studies include both message-passing and data-parallel
versions.
Our objectives are to
provide insight into implementation and performance
issues for image processing applications on general-purpose
message-passing architectures, to investigate implications
on 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.