Parallel Implementation of Geodesic Distance Transform with Application in Superpixel Segmentation

This paper presents a parallel implementation of geodesic distance transform using OpenMP. We show how a sequential-based chamfer distance algorithm can be executed on parallel processing units with shared memory such as multiple cores on a modern CPU. Experimental results show a speedup of 2.6 times on a quad-core machine can be achieved without loss in accuracy. This work forms part of a C implementation for geodesic superpixel segmentation of natural images.