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Study on Fabric Texture Representation and Automatic Identification

Author: YaoFang
Tutor: LiLiQing
School: Donghua University
Course: Textile Engineering
Keywords: Texture features Adaptive Wavelet Wavelet Transform Three decomposition Defect detection
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 147
Quote: 2
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Abstract


Fabric production process, quality control of the fabric, such as defect detection, fuzz ball rating, fabric folds evaluation is very important, however traditional detection Fung to rely on manual detection, false detection rate and leakage due to human factors, higher detection rate. With the extensive application of computer technology, the fabric texture analysis to achieve the automation and intelligence, the theoretical basis of the digitized representation of the fabric texture. Fabric image texture characterization analysis directly determine the effect of the application of fabric tissue parameters and automatic defect detection. Fabric texture characterization and analysis in the analysis and detection of computer vision is applied to textiles appearance worthy of further study, an important application of digital textile value. The fabric texture is a cyclical texture, wavelet transform is cyclical characteristics of the texture image, said one of the main method. This article is based on adaptive wavelet texture image feature extraction, and improve the shortcomings of traditional Porgy non-adaptive, and verify the feasibility and effectiveness of the fabric texture characterization algorithm from the point of view of the fabric defect detection image. In this paper, of Adaptive Wavelet three decomposition method to characterize the texture. First, a brief wavelet analysis machine fabric texture characterization and validation of defect detection, adaptive wavelet analysis for fabric texture characterization by comparison found. Second, pretreatment of fabric image by the approximation of the conditions to find a filter fabric texture match that the wavelet coefficients texture direction volatility and GLCM proposes two new constraints, with common energy and wavelet coefficient poor approximation conditions compared this method works better. Then, with regard to the determination of the adaptive wavelet decomposition level, breaking the previous adaptive wavelet monolayer decomposition degree of entropy decrease as the decomposition of the termination signal to determine the decomposition level three. Determine texture characterization method for the the Adaptive Wavelet three decomposition fabric defect detection, for example, to determine the effectiveness of the method. Finally, apply this texture characterization, plain and twill fabric defect detection, extract the eigenvalue decomposition of fabric image, to determine the existence of the defect through the characteristic values, find a suitable threshold segmentation method based on the comparison of several defect The binarization method as the one-dimensional maximum entropy method. The use of the oval the target defect part of the area, the size of the horizontal direction of the angle between the long and short axes ratio, pursuant to which the analysis of the type and size of the defect characteristics. The experimental results show that the method utilized herein, the characterization of fabric texture is feasible and effective.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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