Paper Info
Title
Low-level image analysis tasks on fine-grained tree-structured SIMD machines
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.
Year
DOI
Venue
1987
10.1016/0743-7315(87)90030-X
J. Parallel Distrib. Comput.
Keywords
Field
DocType
fine-grained tree-structured simd machine,low-level image analysis task,image analysis,tree structure
Data deduplication,Pipeline (computing),Associative property,Massively parallel,Computer science,Parallel computing,SIMD,Thresholding,Connected-component labeling,Very-large-scale integration,Distributed computing
Journal
Volume
Issue
ISSN
4
6
Journal of Parallel and Distributed Computing
Citations 
PageRank 
References 
7
0.75
6
Authors
3
Name
Order
Citations
PageRank
Hussein A H Ibrahim13810.92
John R. Kender2627138.04
David Elliot Shaw3890139.33