Wool/cashmere identification based on projection curves

Zhong, Yueqi; Lü, Kai; Jiang, Tian; Hong, Zhu

doi:10.1177/0040517516658516

Cited by 32 publications

(24 citation statements)

References 15 publications

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“…15 SVMs try to construct a hyperplane and the sample points are mapped to a high dimensional feature space by a nonlinear mapping u: Rn !H and linear regression is applied in the high dimensional feature space to get the nonlinear regression estimation in the original space. 16 An SVM has been used in many pattern recognition and regression estimation problems and has been applied to the problems of dependency estimation and forecasting. 17 The LSSVM is an alternate formulation of SVM classification proposed by Suykens.…”

Section: Forecasting Modelsmentioning

confidence: 99%

A hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of the 3D additive screen printing of athletic shoes

Wang

Liu

Sun

2019

Textile Research Journal

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This paper presents a hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of 3D additive screen printing of athletic shoes. 3D additive screen printing is mainly used in the high-end athletic shoes and clothes field. It requires overlapping and overprinting dozens of times to make the printed patterns stereoscopic. The process of 3D additive screen printing is complex and variable and the production cycle is long. Because of the variability of the screen printing process and the coupling between process parameters, there is no simple method to guide the trial production of new products and obtain the optimal process parameters of screen printing. Trial-and-error is often used but it expends a lot of manpower, materials, and financial resources. To solve the optimization problem, a Taguchi experiment based on fuzzy comprehensive evaluation with five factors and two responses was first designed. Then, a back-propagation network (BPN), least-squares support-vector machine (LSSVM), and random forest (RF) were trained with experimental data to obtain a forecasting model for the process parameters. On comparison, the RF forecasting model performed best in this case. Then, the multi-objective antlion optimizer (MOALO), which is a new multi-objective optimization algorithm with excellent performance, was improved to the IMOALO, and it was proved that IMOALO has a better performance than MOALO. Combining the RF forecasting model with IMOALO, and carrying out the optimization, the optimal process parameters were obtained. Actual printing production shows that the proposed hybrid intelligence technique improves the production efficiency and first pass yield of printed products.

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Section: Forecasting Modelsmentioning

confidence: 99%

A hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of the 3D additive screen printing of athletic shoes

Wang

Liu

Sun

2019

Textile Research Journal

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“…The features have been used to monitor the supervised classification methods which are kernel ridge regression/classification, support vector machine (SVM) and a neural network with multilayer perceptron (MLP). Results proved that the designed projection curves could be used in automatic cashmere/wool identification [13]. She et al presented a novel method which uses the non‐linear demarcation functions with an artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Identification of wool and mohair fibres with texture feature extraction and deep learning

Yildiz

2020

IET Image Processing

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“…Zhong et al proposed a novel method for the identification of wool and cashmere based on projection curves. They used three different methods to reveal the embedded numerical features of the mathematical replica and adopted the SVM algorithm to recognize the fiber types [13]. Lu et al used bag-of-words and spatial pyramid match for the identification of micrographs of cashmere and wool.…”

Section: Introductionmentioning

confidence: 99%

Identification of Extremely Similar Animal Fibers Based on Matched Filter and HOG-SVM

et al. 2019

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Identification of extremely similar animal fibers has always been one of the challenging research topics in the textile field. In this paper, an improved image-based recognition method was proposed for the identification of extremely similar animal fibers including cashmere and wool. A total of 100 groups of wool and 100 groups of cashmere fiber images were collected using the self-developed microscope image analysis system. The contrast of original fiber micro images usually was not high enough and some impurities always existed during the slicing process, so the matched filters were first applied for these images to extract the enhanced fiber binary texture, which only needs to set reasonable segment length and the threshold for different fiber images to eliminate impurities and background. Then, the high-dimensional texture features of the original color images, gray images, and the images processed by matched filter were extracted by calculating and analyzing the histogram of oriented gradient (HOG). The 200 sets of original color images, gray images, and processed images were divided into the training set and testing set according to different proportions, and the recognition expert system based on the support vector machine (SVM) could be trained and validated accordingly. The experimental results show that the recognition accuracy of the fiber images processed by matched filter was obviously improved compared with that of the other two data sets, and the recognition rate reaches the highest with 92.5%. It also proves that the proposed algorithm in this paper can classify and identify extremely similar wool and cashmere fibers more quickly and effectively compared with other texture feature extraction and identification algorithms. INDEX TERMS Extremely similar fibers, image identification, matched filter, HOG, SVM.

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Wool/cashmere identification based on projection curves

Cited by 32 publications

References 15 publications

A hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of the 3D additive screen printing of athletic shoes

A hybrid intelligence technique based on the Taguchi method for multi-objective process parameter optimization of the 3D additive screen printing of athletic shoes

Identification of wool and mohair fibres with texture feature extraction and deep learning

Identification of Extremely Similar Animal Fibers Based on Matched Filter and HOG-SVM

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