Parallel Implementation Of The Split And Merge Algorithm On Hypercube Processors For Object Detection And Recognition

21 March 1989

proceedings article
Published by SPIE-Intl Soc Optical Eng

Vol. 1095, 251-263
https://doi.org/10.1117/12.969276

Abstract

Split and merge is a computationaly efficient region segmentation technique suitable to detect objects or surfaces in a given image. Despite its superior performance, it suffers from large memory usage and excessive computation time. This paper describes parallel implementation of the split and merge algorithm in a 16 node hypercube processor in order to reduce processing time to an acceptable level in the real time applications. Three methods are proposed to parallelize the operation of the method using the nearest neghbor (mesh) topology that can be mapped onto the hypercube architecture. Comparison of the described techniques is given and processing results of the real world images are presented.© (1989) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Keywords

DATA STORAGE

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