Effects of static and dynamic topologies in Particle Swarm Optimisation for edge detection in noisy images

Abstract

Edge detection is an important area in computer vision and detecting continuous edges in noisy images is a hard problem. The Canonical Particle Swarm Optimisation (CanPSO) has been used for edge detection since 2009. Although the Bare Bones PSO (BBPSO) and the Fully Informed Particle Swarm (FIPS), as two well-known versions of PSO, have interesting features to overcome noise, they have never been applied to edge detection in noisy images. In this paper, six different static topologies along with two dynamic topologies are implemented within the three versions of PSO and their effects are investigated in a PSO-based edge detector in noisy images. Computational experiments show that FIPS with the toroidal topology outperforms the canonical and bare bones PSO with various static and dynamic topologies in most cases and is more robust to noise.