Abstract��Existed kernel methods were introduced and studied, and a new kernel function was presented and applied to feature extraction of slab surface defects. SVM was used to classify the images. Experimental results show that the feature extracted by new kernel function gets the highest classification rate of 91.55%.
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E-mail: 1341506193@qq.com
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��ﴺ. ����KPCA�İ�������ȱ��ʶ��[J]. �й������ڿ���, 2014, 32(2): 25-27.
MA Feng-chun. Recognition Method of Slab Surface Defect Based on KPCA. Chinese Journal of Iron and Steel, 2014, 32(2): 25-27.
2014, Vol. 32 
