Detection of continuous, smooth and thin edges in noisy images using constrained particle swarm optimisation

  • Authors:

  • Mahdi Setayesh;Mengjie Zhang;Mark Johnston

  • Affiliations:

  • Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand

  • Venue:
  • Proceedings of the 13th annual conference on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

Detecting continuous edges is a hard problem especially in noisy images. We propose an algorithm based on particle swarm optimisation (PSO) to detect continuous and smooth edges in such images. A constrained PSO-based algorithm with a new penalised objective function and two constraints is proposed to overcome noise and reduce broken edges. The new algorithm is examined and compared with a modified version of the Canny algorithm, the robust rank order (RRO)-based algorithm, and an existing PSO-based algorithm on two sets of images with different types and levels of noise. The results suggest that the new algorithm detect edges more accurately than these three algorithms and the detected edges are smoother than those detected by the previous PSO algorithm and thinner than those detected by RRO.