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Proceedings Paper

Issues in parallelism in object recognition
Author(s): Suchendra M. Bhandarkar; Minsoo Suk
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Paper Abstract

Serial implementation of conventional object recognition techniques such as Hough Clustering and Interpretation Tree search perform poorly when faced with the combinatorial explosion of the search space especially in multiple-object scenes with partial occlusion. Parallelization of object recognition techniques therefore is an attractive proposition. The two issues that concern data parallelism are choice of multiprocessing granularity and choice of multiprocessing control. This paper shows a direct correspondence between the the choice of multiprocessing granularity and the granularity of representation of the image features and the object model features and also the direct correspondence between the choice of multiprocessing control and choice of constraint propagation technique. This paper cites two examples of parallelization of object recognition techniques - one based on Jjough Clustering and the other on Interpretation Tree search. Both examples are examined in the light of the two issues that pertain to data parallelism.

Paper Details

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1384, High-Speed Inspection Architectures, Barcoding, and Character Recognition, (1 February 1991); doi: 10.1117/12.25338
Show Author Affiliations
Suchendra M. Bhandarkar, Univ. of Georgia (United States)
Minsoo Suk, Syracuse Univ. (United States)

Published in SPIE Proceedings Vol. 1384:
High-Speed Inspection Architectures, Barcoding, and Character Recognition
Michael J. W. Chen, Editor(s)

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