Parallel Implementation of Vision Algorithms on Workstation Clusters (1994)
| Venue: | in Proceeding. 12th International Conference on Pattern Recognition |
| Citations: | 7 - 4 self |
BibTeX
@INPROCEEDINGS{Judd94parallelimplementation,
author = {Dan Judd and Nalini K. Ratha and Philip K. Mckinley and John Weng and Anil K. Jain},
title = {Parallel Implementation of Vision Algorithms on Workstation Clusters},
booktitle = {in Proceeding. 12th International Conference on Pattern Recognition},
year = {1994},
pages = {317--321},
publisher = {IEEE Press}
}
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Abstract
Parallel implementations of two computer vision algorithms on distributed cluster platforms are described. The first algorithm is a square-error data clustering method whose parallel implementation is based on the well-known sequential CLUSTER program. The second algorithm is a motion parameter estimation algorithm used to determine correspondence between two images taken of the same scene. Both algorithms have been implemented and tested on cluster platforms using the PVM package. Performance measurements demonstrate that it is possible to attain good performance in terms of execution time and speedup for large-scale problems, provided that adequate memory, swap space, and I/O capacity are available at each node. 1 Introduction Many tasks in computer vision are computationally intensive. In order to reduce the total time required to solve these problems, special-purpose hardware or vector supercomputers have often been used. Recently, parallel processing has started to dominate the ...
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