Aspects of computational mode and data distribution for parallel range image segmentation

Abstract

Parallel processing methods are a means to achieve significant 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-off between the number of data transfers versus the amount of computation. This trade-off is examined both analytically and experimentally. Additionally, using the same initial data distributions, a technique is introduced for changing the allocation of work to each of the processors to reduce the number of network settings by one half. This technique and the method for determining the better initial data distribution can be used with any machine and any window-based technique that requires a full window to perform image calculations. Comparisons of range image processing algorithms are performed using “pure” SIMD algorithms, “pure” MIMD algorithms, and mixed-mode implementations with both SIMD and MIMD elements. Each of these approaches are quantitatively analyzed and compared for implementing the different phases of a particular hybrid range segmentation algorithm. Results of this implementation study indicate that quantifiable reductions in execution time result from the proper choice of parallel mode for each portion of the segmentation process.