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.

Xin Yang

Papers

Analysis on niche genetic algorithm based nonparametric curve recognition