Neural-Fuzzy Classification for Fabric Defects
2001
Huang, Chang-Chiun | Chen, I-Chun
Image classification by a neural-fuzzy system is presented for normal fabrics and eight kinds of fabric defects. This system combines the fuzzification technique with fuzzy logic and a back-propagation learning algorithm with neural networks. Four input features—the ratio of projection lengths in the horizontal and vertical directions, the gray-level mean and standard deviation of the image, and the large number emphasis (LNE) based on the neighboring gray level dependence matrix for the defect area—are selected and their usefulness is justified. The neural network is also implemented and compared with the neural-fuzzy system. The results demonstrate that the neural-fuzzy system is superior to the neural network in classification ability.
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