โ Scribed by Nicholas Giolmas; Daniel W. Watson; David M. Chelberg; Peter V. Henstock; June Ho Yi; Howard Jay Siegel
No coin nor oath required. For personal study only.
Parallel processing methods are a means to achieve signiยฎcant speedup of computationally expensive image understanding algorithms, such as those applied to range images. Practical implementations of these algorithms must deal with the problems of selecting an appropriate parallel architecture and mapping the algorithm onto that architecture. The parallel implementation approaches for range image segmentation that are presented here are applicable to many low-level image understanding algorithms in a variety of parallel architectures. An evaluation of initial data distribution is presented to determine whether a square subimage or a striped subimage distribution would result in the greatest overall reduction in execution time for the given range image segmentation problem. Novel implementations that consider each data distribution's treatment of edge pixels in window operations yield a trade-o between the number of data transfers versus the amount of computation. This trade-o is examined both analytically and experimentally. Additionally, using the same initial data distributions, a Parallel Computing 25 (1999) 499ยฑ523 www.elsevier.com/locate/parco
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