Performance of optimized fuzzy edge detectors using particle swarm algorithm

Authors:
Noor Elaiza Abdul Khalid;Mazani Manaf
Affiliations:
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia;Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 2
Cited 0

Digital Image Processing

Digital Image Processing
Gaussian Rule Based Fuzzy (GRBF) Membership Edge Detection on Hand Phantom Radiograph Images

CGIV '08 Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The purpose of the paper is to compare the performance of various fuzzy edge detectors which have been optimized by Particle Swarm Optimization (PSO) Three different type edge detectors Classical fuzzy Heuristic (CFH), Gaussian rule based (GRBF) and Robust Fuzzy Complement (RFC) are used These edge detectors are effective in detecting edges, however the edges are thick This paper proposes the used of particle swarm optimization algorithm as a method of producing thin and measurable edges The fuzzy edge detectors are used in the initial swarm population and the objective function The performance is based on the consistency of the visual appearance, fuzzy membership threshold and the number of complete edges detected All three optimized edge detector performs reasonably well but CFHPSO outperform the rest.