Characterization Of Fabric Appearances Based On Digital Image Analysis

In order to improve the reliability and reproducibility of textile industry products,evaluating appearance characteristics objectively on fabrics plays a more and moreimportant role in textile industry, such as characteristics of defect, pilling, organizationstructure, color and roughness, et al. Processing technology of digital image is appliedto three aspects on the basis of theory research including automatic defect detection andclassification of woven material, objective rating system on fabric pilling, fabricstructure recognition and classification in this article. Main research results are obtainedas follows:1. Joint fabric defect detection method based on method library. A joint fabricdefect detection method based on "method library" is proposed, and various effectivedefect detection algorithms on fabrics are improved and synthesized in joint fabricdefect detection method based on method library. The proposed method integratesGabor-Gauss method, background analysis, multi-scale wavelet method as joint defectdetection method library to detect defects on fabrics. A preliminary graphical userinterface (GUI) based on method library is designed in order to facilitatehuman-computer interactions.2. Fabric defect classification algorithm based on combining texture feature oflocal binary pattern (LBP) and Tamura. Fabric defect classification algorithm viacombining texture feature of local binary pattern (LBP) and Tamura texture feature isput forward due to a single feature can not be effective description of fabric defect.Local binary patterns can describe characteristics of fabric texture from local, Globaldefect texture feature could be represented by way of Tamura method. Better descriptionof defect texture feature could be obtained by means of combining local binary pattern(LBP) and Tamura. Conjugate BP is applied to train and test extracted feature vector inproposed method.3. Defect detection on printing fabrics. The printing fabric can be divided intocycle printing fabric and random printing fabric. In the test of cycle printing fabrics,moving window is set in regular band based on the information of periodic patternfabric. Energy and variance of defect-free fabrics could be determined as threshold.Calculation of energy and variance of sample cycle printing fabrics could realize defectdetection based on obtained threshold. In the testing of random printing fabrics,parameters of Gabor filter can be selected via genetic algorithm, Gabor filter structuredby optimal parameters is matched with fabric texture so as to extract the effective information of woven fabric in fabric defect detection, thus the purpose of fabric defectsegmentation can be achieved.4. A new method based on wavelet transform and local binary pattern (LBP) wasproposed for fabric pilling objective evaluation. The surface of the fabric pillinginformation was inspected by using two-dimensional discrete wavelet transform(2DDWT). The pilling feature vectors were consisted of the wavelet energy value whichis the wavelet decomposition of sub-image details in scale4to6with three directions(horizontally, vertically, diagonally) and the LBP features which is waveletreconstruction image in scale3to6using LBP. It is necessary to normalization thefeatures using principal component analysis (PCA) to reduce the dimensions. Then, theprocessed feature can act as fabric pilling classification data input for support vectormachine (SVM).5. An automatic and real-time classification method is proposed to analyze threewoven fabrics such as plain, twill, and satin weave. The methodology involves twoapproaches to extract texture features using gray-level co-occurrence matrix (GLCM)and Gabor wavelet. Then, principal component analysis (PCA) is utilized to deal withthe texture feature vectors to gain minimize redundancy and maximize principalcomponent feature vectors. Finally, for the classification phase, probabilistic neuralnetwork (PNN) is applied to classify three basic woven fabrics. With strong real-time,robustness, fault-tolerance and non-linear classification capability, PNN can be apromising tool for classification of woven fabrics. The experimental results show thatPNN classifier with faster training speed can classify woven fabrics accurately andefficiently. Besides, compared with GLCM method and Gabor wavelet method, thefusion of the two feature vectors obtain the best classification result (95%).