Image Edge Detection Using Variation-Adaptive Ant Colony Optimization

  • Jing Tian
  • Weiyu Yu
  • Li Chen
  • Lihong Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6910)

Abstract

Ant colony optimization (ACO) is an optimization algorithm inspired by the natural collective behavior of ant species. The ACO technique is exploited in this paper to develop a novel image edge detection approach. The proposed approach is able to establish a pheromone matrix that represents the edge presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the image’s intensity values. Extensive experimental results are provided to demonstrate the superior performance of the proposed approach.

Keywords

Saliency Detection Heuristic Information Pheromone Matrix Evaporation Factor Image Edge Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Tian
    • 1
  • Weiyu Yu
    • 2
  • Li Chen
    • 1
  • Lihong Ma
    • 3
  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyP.R. China
  2. 2.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouP.R. China
  3. 3.Guangdong Key Lab of Wireless Network and Terminal, School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouP.R. China

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