Affiliation:
1. Department of Textile Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China
Abstract
Classification of seven kinds of dyeing defects is proposed using image processing and fuzzy neural network approaches. The defects include filling band in shade, dye and carrier spots, mist, oil stain, tailing, listing, and uneven dyeing on selvage. The fuzzy neural classification system is constructed by a fuzzy expert system with the neural network as a fuzzy inference engine. The neural network is trained to become the inference engine using sample data. This fuzzy neural network system possesses merits of both fuzzy logic and neural networks, and thus is more intelligent in handling pattern recognition and classification problems. Region growing is adopted to directly detect different defect regions in an image. In all, seventy samples, ten samples for each defect, are obtained for training and testing. The results demonstrate that the fuzzy neural network approach can precisely classify these samples by the features selected.
Subject
Polymers and Plastics,Chemical Engineering (miscellaneous)
Cited by
25 articles.
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