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Abstract:Sample images of hot-rolled steel plates are decomposed into multiple subbands with different scales and directions by Tetrolet transform. The high-pass Tetrolet coefficients of subbands are combined into a high-dimensional feature vector. Kernel locality preserving projection(KLPP) is applied to the high-dimensional feature vector for dimension reduction, which results in a low-dimensional feature vector. The low-dimensional feature vector is fed into support vector machine(SVM) fordoi:10.3901/jme.2016.04.013 fatcat:gbceqenurva7zhukpqiztdkku4