Paper Title:
A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target
  Abstract

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

  Info
Periodical
Chapter
Chapter 1: Transportation & Service Science
Edited by
Robin G. Qiu and Yongfeng Ju
Pages
50-55
DOI
10.4028/www.scientific.net/AMM.135-136.50
Citation
Y. B. Hou, Y. Meng, J. B. Mao, "A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target", Applied Mechanics and Materials, Vols. 135-136, pp. 50-55, 2012
Online since
October 2011
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Price
$32.00
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