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Proceedings Paper

Analysis on niche genetic algorithm based nonparametric curve recognition
Author(s): Wei Wei; Ming Yu; Xin Yang
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Paper Abstract

Niche Genetic Algorithm (NGA) is proposed to recognize a disconnected nonparametric curve from a noisy binary image. The fitness function used in the NGA is derived from the hypothesis: Human Visual Tradition Model (HVTM). Sharing function based niche technique and elite-preserving strategy are utilized to preserve population variety for converging at the global optimum. It has the advantage of using a nonparametric method to extract disconnected curves from the noisy binary image other than the parametric method, which Hough Transform (HT) can conclude. The curve extracted by using the nonparametric method is verified by comparing the best strings respectively along rows and columns in the permutation-based encoding space. The curve length can be derived automatically from the image by calculating the accumulation of the distance between the neighbor tiles in the extracted curve. In this paper, it is analyzed that the odd order moments of the tiles in the raw image is more sensitive to the tiles with cracks other than tiles with only noise, the algorithm complexity is sensitive to encoding approach, the evolution converge characteristics are sensitive to sharing function and parameters in fitness function. Experimental results present that the approach was successfully used in pavement crack detection.

Paper Details

Date Published: 22 January 2008
PDF: 12 pages
Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 683311 (22 January 2008); doi: 10.1117/12.754076
Show Author Affiliations
Wei Wei, Hebei Univ. of Technology (China)
Ming Yu, Hebei Univ. of Technology (China)
Xin Yang, Hebei Univ. of Technology (China)

Published in SPIE Proceedings Vol. 6833:
Electronic Imaging and Multimedia Technology V
Liwei Zhou; Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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