Coal Dust Recognition Based on Concave Point Extraction and Ellipse Fitting

Abstract:

A new coal dust particle recognition algorithm based on concave points extraction and ellipse fitting is proposed for the features of irregularities and particle overlap. The new algorithm includes contour processing and ellipse fitting in this paper. In the part of contour processing, the feature points are obtained with polygonal approximation on the edge of a binary dust particles image, and then concave points of overlapping particles are extracted by the method of angle combined with size, finally the edge is segmented by concave points. To solve the problem that direct least square ellipse fitting is easily affected by noise points, bare bones particle swarm optimization is introduced to find global optimum fitting parameters and the segmented edge is ellipse fitted. Experiment results show this proposed algorithm obtains better recognition performance.

• Periodical: Applied Mechanics and Materials (Volume 654)
• Pages: 296-299
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.654.296
• Online since: October 2014
• Authors: Wei Zhang*, Hong Bo Yi, Xiao Wen Wang
• Keywords: Bare Bones Particle Swarm Optimization, Concave Point, Fitting Ellipse, Particle Recognition, Polygon Approximation

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