Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network

In this paper, an approach to grade nonwoven uniformity by combining wavelet texture analysis and learning vector quantization (LVQ) neural network is proposed. Six hundred and twenty-five nonwoven images of five different grades, 125 images of each grade, are decomposed at four different levels with five wavelet bases of Daubechies family, and two kinds of energy values L1 and L2 extracted from the high frequency subbands are used as the input features of the LVQ neural network solely and jointly. For each grade, 60 comparative experiments are employed to evaluate the performance of our method, which takes into account three effect factors, wavelet base (the length of filter), decomposition level and feature set. Experimental results on the 625 nonwoven images indicate that just use L1 as feature calculated with db6, at level 3, the identification accuracy of grade A, grade C and grade E are 100%. When the nonwoven images are decomposed at level 3, the minimal average identification accuracy of five grades with five different wavelet bases is 87.7%.