Smoothing filter is the method of choice for image
preprocessing and pattern recognition. We present a
new concurrent method for smoothing 2D object in
binary case. Proposed method provides a parallel
computation while preserving the topology by using
homotopic transformations. We introduce an adapted
parallelization strategy called split, distribute and
merge (SDM) strategy which allows efficient
parallelization of a large class of topological
operators including, mainly, smoothing,
skeletonization, and watershed algorithms. To achieve
a good speedup, we cared about task scheduling.
Distributed work during smoothing process is done by
a variable number of threads. Tests on 2D binary
image (512*512), using shared memory parallel
machine (SMPM) with 8 CPU cores (2× Xeon E5405
running at frequency of 2 GHz), showed an
enhancement of 5.2 thus a cadency of 32 images per
second is achieved.
In miscellaneous applications of image treatment, thinning and crest restoring present a lot of interests. Recommended
algorithms for these procedures are those able to act directly over grayscales images while preserving topology. But their
strong consummation in term of time remains the major disadvantage in their choice. In this paper we present an efficient
hardware implementation on RISC processor of two powerful algorithms of thinning and crest restoring developed by
our team. Proposed implementation enhances execution time. A chain of segmentation applied to medical imaging will
serve as a concrete example to illustrate the improvements brought thanks to the optimization techniques in both
algorithm and architectural levels. The particular use of the SSE instruction set relative to the X86_32 processors (PIV
3.06 GHz) will allow a best performance for real time processing: a cadency of 33 images (512*512) per second is
assured.
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