Parallel image thinning through topological operators on shared memory parallel machines

TL;DR

This paper introduces a parallel implementation of a topological image thinning operator that efficiently processes grayscale images on shared memory parallel machines, achieving significant speedup and supporting various topological operations.

Contribution

The paper presents a novel parallelization methodology combining SDM strategy and mixed parallelism for topological image thinning, enabling efficient processing without altering image topology.

Findings

Achieved a 6.2x speedup on 2D grayscale images.

Processed up to 125 images per second with 8 threads.

Supported a range of topological operators including skeletonization.

Abstract

In this paper, we present a concurrent implementation of a powerful topological thinning operator. This operator is able to act directly over grayscale images without modifying their topology. We introduce an adapted parallelization methodology which combines split, distribute and merge (SDM) strategy and mixed parallelism techniques (data and thread parallelism). The introduced strategy allows efficient parallelization of a large class of topological operators including, mainly, {\lambda}-leveling, skeletonization and crest restoring algorithms. To achieve a good speedup, we cared about coordination of threads. Distributed work during thinning process is done by a variable number of threads. Tests on 2D grayscale image (512*512), using shared memory parallel machine (SMPM) with 8 CPU cores (2x Xeon E5405 running at frequency of 2 GHz), showed an enhancement of 6.2 with a maximum achieved cadency of 125 images/s using 8 threads.