Abstract
This paper examines the applicability of fine-grained tree-structured SIMD machines, which are amenable to highly efficient VLSI implementation, to several low-level image understanding tasks. Algorithms are presented for histogramming, thresholding, image correlation, connected component labeling, and computing Euler number. A particular massively parallel machine called NON-VON is used for purposes of explication and performance evaluation. Only NON-VON tree-structured communication capabilities and its SIMD mode of execution are considered in this paper. Novel algorithmic techniques are described, such as vertical pipelining, subproblem partitioning, associative matching, and data duplication, that effectively exploit the massive parallelism available in fine-grained SIMD tree machines while avoiding communication bottlenecks. Simulation results are presented and compared with results obtained or forecast for other highly parallel machines. The relative advantages and limitations of the class of machines under consideration are outlined; except for some types of image correlation, the fine-grained SIMD tree is exceptionally fast.
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