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Parallel Algorithms for Image Enhancement and Segmentation by
Region Growing with an Experimental Study
David A. Bader
dbader@umiacs.umd.edu
Joseph JáJá
joseph@umiacs.umd.edu
David Harwood
harwood@umiacs.umd.edu
Larry S. Davis
lsd@umiacs.umd.edu
Institute for Advanced Computer Studies
University of Maryland, College Park, MD 20742
Fri May 12 13:24:35 EDT 1995
Abstract:
This paper presents efficient and portable implementations of a useful
image enhancement process, the Symmetric Neighborhood Filter (SNF),
and an image segmentation technique which makes use of the SNF and a
variant of the conventional connected components algorithm which we call
-Connected Components . Our general framework is a single-address space, distributed
memory programming model. We use efficient techniques for
distributing and coalescing data as well as efficient combinations of
task and data parallelism. The image segmentation algorithm makes use
of an efficient connected components algorithm which uses a novel
approach for parallel merging. The algorithms have been coded in
SPLIT-C and run on a variety of platforms, including the Thinking
Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific
CS-2, Intel Paragon, and workstation clusters. Our experimental
results are consistent with the theoretical analysis (and provide the
best known execution times for segmentation, even when compared with
machine-specific implementations.) Our test data include difficult
images from the Landsat Thematic Mapper (TM) satellite data. More
efficient implementations of SPLIT-C will likely result in even
faster execution times.
Keywords: Parallel Algorithms, Image Processing, Region Growing,
Image Enhancement, Image Segmentation, Symmetric Neighborhood Filter,
Connected Components, Parallel Performance.
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David A. Bader
dbader@umiacs.umd.edu