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Proceedings Paper

Orthogonal access architectures and reduced meshes for parallel image computations
Author(s): Hussein M. Alnuweiri; V. K. Prasanna Kumar
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Paper Abstract

A class of orthogonal-access parallel organizations is studied for applications in image and vision analysis. These architectures consist of a massive memory and a reduced number of processors which access the shared memory. The memory can be envisaged as an array of memory modules in the k-dimensional space, with each row of modules along a certain dimension connected to one bus. Each processor has access to one bus along each dimension. It is shown that these organizations are communication-efficient and can provide processor time optimal solutions to a wide class of image and vision problems. In the two-dimensional case, the basic organization has ii processors and an n x n memory array which can hold an n x n image, and it provides 0(n) time solution to several image computations including: histograming, histogram equalization, computing connected components, convexity problems, and computing distances. Such problems also take 0(n) time on a two-dimensional mesh with n2 processors. For the general k-dimensional case, a class of orthogonal data movement operations can be implemented on such organizations to yield processor-time optimal image and vision algorithms.

Paper Details

Date Published: 1 July 1990
PDF: 12 pages
Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); doi: 10.1117/12.19581
Show Author Affiliations
Hussein M. Alnuweiri, King Fahd Univ. of Petroleum and Minerals (Saudi Arabia)
V. K. Prasanna Kumar, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 1246:
Parallel Architectures for Image Processing
Joydeep Ghosh; Colin G. Harrison, Editor(s)

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