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Title An Efficient Parallel Algorithm for Computing the Gaussian Convolution of Multi-dimensional Image Data
Edition Volume 14, Number 3
Call Number
ISBN/ISSN 0920-8542
Author(s) HOI-MAN YIP
ISHFAQ AHMAD
TING-CHUEN PONG
Subject(s)
Classification
Series Title The Journal of Supercomputing
GMD Electronic Journal
Language English
Publisher Springer Netherlands
Publishing Year 1999
Publishing Place Netherlands
Collation 23p
Abstract/Notes In this paper, we propose a parallel convolution algorithm for estimating the partial derivatives
of 2D and 3D images on distributed-memory MIMD architectures. Exploiting the separable characteristics
of the Gaussian filter, the proposed algorithm consists of multiple phases such that each phase
corresponds to a separated filter. Furthermore, it exploits both the task and data parallelism, and reduces
communication through data redistribution. We have implemented the proposed algorithm on the Intel
Paragon and obtained a substantial speedup using more than 100 processors. The performance of the
algorithm is also evaluated analytically. The analytical results confirming with the experimental results
indicate that the proposed algorithm scales very well with the problem size and number of processors.
We have also applied our algorithm to the design and implementation of an efficient parallel scheme
for the 3D surface tracking process. Although our focus is on 3D image data, the algorithm is also applicable
to 2D image data, and can be useful for a myriad of important applications including medical
imaging, magnetic resonance imaging, ultrasonic imagery, scientific visualization, and image sequence
analysis.
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