PARTICLE SWARM FOR PATTERN MATCHING IN IMAGE ANALYSIS
Luca Mussi, Stefano Cagnoni · 2009
This work presents a preliminary investigation on the use of a Particle Swarm Optimization (PSO) algorithm variant for Pattern Matching in image analysis. Providing each particle with its own target and having them organized with the classical Von Neumann topology is shown to be a feasible way to obtain a swarm able to locate a pattern on a digital image. Some preliminary tests on synthetic images show the effectiveness of the modified swarm algorithm, highlighting its insensitivity to basic transforms like mirroring, scaling and perspective deformations of the pattern.