CS-TR-3449, UMIACS-TR-95-44
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 delta-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.
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