| At present, the work to analyze fabric structure still depends on artificial visual measurement, which is easily influenced by personal sight, mood, mental state as well as optical line. Therefore this method of testing is of great individual subjectivity and unreliability. The recognition quality is difficult to guarantee as a result of that. Meanwhile, it takes much more time for operators to do the same testing work repeatedly with poor efficiency, which cannot meet the needs of current textile production for small quantities, multi-item and high efficiency. With so many drawbacks, artificial visual measurement has become a great bottleneck of textile industry to improve information technology and production automation. An automatic identification system on fabric structural parameters will realize automation fully and enhance textiles'production efficiency powerfully. With the development of image processing technology and artificial intelligence, automatic analysis on fabric structure as a replacement of manual labor is of great possibility.In this thesis, features of fabric-image have been extracted by GLCM (Gray Level Co-occurrence Matrix). These features were employed to a three layer BP neural network for analyzing. It was verified by experiments on three kinds of fabric structures, such as plain weave, cross grain and satin, that all the structures can be correctly identified up to the rate of 93.45%. Furthermore, wavelet decomposition theory was applied to extract fabric image brightness information to locate point area. Related features could be drawn from the surface texture information of interlacing images, hence the properties of warp and weft can be judged to analyze the specific textual structure of fabric. |
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