Ming Yu

Bio: Ming Yu is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Nonparametric statistics & Parametric statistics. The author has co-authored 1 publications.

Analysis on niche genetic algorithm based nonparametric curve recognition

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.