| Wrinkling is caused by twisting, shearing, compressing and bending, etc in the process of washing, drying, or wearing, which is difficult to recover even when external force is removed. The ability for the fabric to resist wrinkling is called wrinkle resistance. The behavior of wrinkle resistance is not only the basic performances of fabric, but also is one of the most important characteristics in determining the visual aesthetic of clothes. Therefore, it is of vital importance to precisely and objectively measure and evaluate the wrinkle resistance of fabrics. However, the commonly used wrinkle resistance measuring methods have some drawbacks in which the deformation and force direction of fabric are very different from that in actual wear, resulting in that the testing methods can’t be used to measure the ability of fabric to resist wrinkle during actual wear.Aiming at this, a novel fabric wrinkling resistance measuring method simulating actual wear is put forward and a new wrinkle producing instrument is set up in this paper. Furthermore, image process technology is used to analyze the wrinkles produced by the simulating method and wrinkle density, gray level co-occurrence and wavelet analysis parameters are extracted. Then based on information fusion technology, these parameters are combined into a comprehensive wrinkling parameter. Finally, these specifications are used to cluster fabric wrinkling grade with different clustering analysis method and neural networks. The following results have been drawn.1) Fabric wrinkle resistance measuring instrument simulating actual wear is set up, which can produce wrinkle very similar with wrinkles during actual wear in appearance. And there is good agreement between experts’ subjective evaluation results of wrinkles produced by the new method and that in actual wear, including rank, score and grade of wrinkling degree. Besides, variation coefficient of wrinkle density in the new method is smaller in value than that of wrinkle recovery angle. Therefore, the measurement stability of the new method is better than most commonly used the wrinkle recovery angle method.2) In the method of wrinkle recovery angle, only wrinkle resistance in the warp and weft direction of fabric is considered which is unilateral. Aiming at this, it is put forward that wrinkle recovery angle at 45° should be included to improve the consistency of the testing result with actual wear. Moreover, equation between wrinkle recovery angle at different direction with wrinkle density which can be used to predict wrinkling degree during actual wear according to wrinkle recovery angle, avoiding the time and energy consuming fashion making and actual wear experiment.3) Feature and texture of fabric wrinkled image are extracted and analyzed with image processing technology. Results show that there is good agreement between wrinkle density extracted(WD) by Sobel operator detected from gray level image and score of the experts’ subjective evaluation. Wrinkle recovery angle in bias direction also plays import part in fabric wrinkle resistance. Fabric with smoother surface tends to have larger energy and correlation, but smaller entropy and contrast of the gray level co-occurrence matrix(GLCM). The correlation between GLCM parameters and wrinkling degree from large to small is entropy, energy, correlation and contrast in turn. The more severely wrinkled fabric tends to have larger detailed coefficient standard deviation in three horizontal, vertical and diagonal directions through wavelet analysis. The detailed coefficient standard deviation in horizontal direction is larger than that of vertical and diagonal directions in value. Analysis of wrinkle density, GLCM and wavelet transform show that contribution of wrinkle recovery angle in warp direction with in actual wear is larger than that of weft direction.4) Fabric comprehensive wrinkling index CWI is obtained by data fusing from WD,Entropy, SH1 using Information fusion technology. The test of rank correlation coefficient shows that there is good agreement between the rank according to CWI value of 48 fabrics and that of experts’ subjective evaluation. CWI can characterize fabric wrinkle more objectively and comprehensively than the three specifications.5) Three clustering methods are employed to analyze wrinkled image with specifications of WD, Entropy, SH1 and CWI, respectively. When using k-means cluster method and Self-Organizing Feature Map(SOM) neural network method, the result of choosing CWI as specification is better than that of WD,Entropy, SH1, which proves that CWI can characterize what eyes observe on fabric wrinkle. With respect to clustering method, the clustering result of SOM agrees with subjective evaluation results better than that of system and k-means method.6) LVQ and PNN neural network are employed as supervised learning method to analyze fabric wrinkle, with specifications of WD,Entropy, SH1 and CWI, respectively. It proves that the prediction accuracy can be improved when CWI is added into the specifications and the prediction accuracy of VQ-PNN combined neural network is improved significant with the single LVQ and PNN neural network.The measurement for fabric wrinkle simulating actual wear put forward in this paper makes up for the defect in the measurements now commonly used and the agreement between testing results and wrinkling in actual wear can be increased significantly. Besides, a new approach has been provided for the textile measuring field, the testing result of which can be used to instruct fabric design and help reduce the wasted caused by misuse of fabrics. On the other hand, the objective evaluation and automatic recognition of fabric wrinkling is based on image processing and pattern recognition technology, which meets the trend and direction towards information and can promote the development of on-line inspection technology of computer. |
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