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Proceedings Paper

Parallel Implementation Of The Split And Merge Algorithm On Hypercube Processors For Object Detection And Recognition
Author(s): Mehmet Celenk; Prabhashankar Lakshman
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Paper 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.

Paper Details

Date Published: 21 March 1989
PDF: 13 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969276
Show Author Affiliations
Mehmet Celenk, Ohio University (United States)
Prabhashankar Lakshman, Ohio University (United States)


Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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