Optimal image algorithms on an orthogonally-connected memory-based architecture

Hussein M. Alnuweiri, V. K.Prasanna Kumar

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Processor-time optimal algorithms are presented for several image and vision problems. A parallel architecture which combines an orthogonally accessed memory with a linear array structure is used. The organization has p processors and a memory of size O(n2) locations. The number of processors p can vary over the range [1, n3/2] while providing optimal speedup for several problems in image analysis and vision. Such problems include labeling connected regions, computing minimum convex containers of regions, and computing nearest neighbors of pixels and regions. Optimal algorithms are presented for histogramming and computing the Hough transform. Such problems arise in medium-level vision and require global operations or dense data movement. It is shown that for these types of problems, the proposed organization is superior to the mesh and pyramid organizations.

Original languageEnglish
Pages (from-to)350-355
Number of pages6
JournalProceedings - International Conference on Pattern Recognition
Volume2
Publication statusPublished - 1 Dec 1990
EventProceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA
Duration: 16 Jun 199021 Jun 1990

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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