Scalable geometric hashing on MasPar machines

Ashfaq A. Khokhar, Viktor K. Prasanna, Hyoung J. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present scalable data parallel algorithms for geometric hashing. We perform implementations of the proposed algorithms on MasPar MP-1/MP-2. In earlier parallel implementations, the number of processors employed is independent of the size of the scene but depends on the size of the model database which is usually very large. We design new parallel algorithms and map them onto MP-1/MP-2. These techniques significantly improve upon the number of processors employed while achieving superior time performance. Our implementations run on a P processor machine, such that 1 ≤ P ≤ S, where S is the number of feature points in the scene. Our results show that a probe of the recognition phase for a scene consisting of 1024 feature points takes less than 50 m sec on a 1K processor MP-1/MP-2.

Original languageEnglish
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages594-595
Number of pages2
ISBN (Print)0818638826
Publication statusPublished - 1993
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: 1993 Jun 151993 Jun 18

Publication series

NameIEEE Computer Vision and Pattern Recognition

Other

OtherProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period93/6/1593/6/18

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Khokhar, A. A., Prasanna, V. K., & Kim, H. J. (1993). Scalable geometric hashing on MasPar machines. In Anon (Ed.), IEEE Computer Vision and Pattern Recognition (pp. 594-595). (IEEE Computer Vision and Pattern Recognition). Publ by IEEE.