2015
DOI: 10.4028/www.scientific.net/kem.671.385
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Identification of Wool and Cashmere Based on Texture Analysis

Abstract: In order to separate wool from cashmere efficiently, an identification method based on texture analysis was proposed in this paper. The microscopic images captured by CCD digital camera were preprocessed as the texture image. Improved Tamura texture feature were employed to analyzing the final texture images and to attaining the texture parameters. Through a large number of samples, the mathematical modeling was completed by using neural network. Experiment results indicate that texture analysis can be a feasi… Show more

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Cited by 18 publications
(14 citation statements)
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“…Xing et al used the GMRF method to extract the wool and cashmere fiber texture features and the final identification accuracy was 90.07% [15] Jiao et al used the co-occurrence matrix for the texture analysis of fibers and the final identification accuracy was 91.93% [16]. Yuan et al used the Tamura texture analysis method to describe the fiber surface texture feature and the final identification accuracy was 81.17% [17]. Although some algorithms can achieve high recognition rate compared with the method proposed in this paper, they require a large number of sample data sets.…”
Section: B Comparison With the Existing Methods For Fiber Features Ementioning
confidence: 99%
See 1 more Smart Citation
“…Xing et al used the GMRF method to extract the wool and cashmere fiber texture features and the final identification accuracy was 90.07% [15] Jiao et al used the co-occurrence matrix for the texture analysis of fibers and the final identification accuracy was 91.93% [16]. Yuan et al used the Tamura texture analysis method to describe the fiber surface texture feature and the final identification accuracy was 81.17% [17]. Although some algorithms can achieve high recognition rate compared with the method proposed in this paper, they require a large number of sample data sets.…”
Section: B Comparison With the Existing Methods For Fiber Features Ementioning
confidence: 99%
“…Then these texture features would be used as input parameters of support vector machine to realize the recognition of wool and cashmere [16]. Yuan et al used the improved Tamura texture feature to analysis the final texture images and identify the cashmere and wool fibers by neural network [17].…”
Section: Introductionmentioning
confidence: 99%
“…Some techniques, which use the microscopy with human eye, separate these fibres from the samples of their circular scales and others from their chemical and physical characteristics. However, the characteristic features of these samples are still the most useful proof for the capable microscopy to characterize animal fibres such as wool, merino, mohair and cashmere [1–5]. From this perspective, classification of animal fibres is actually a distinctive task of classification and pattern recognition.…”
Section: Introductionmentioning
confidence: 99%
“…Instead of calculating the measurement parameters of fiber surface geometry, some scholars extract visible features from fiber images to describe the difference in scale patterns between wool and cashmere fibers. Yuan et al 11 employed Tamura texture features to describe fiber images and a back propagation (BP) neural network to classify wool and cashmere fibers. Zhong et al 12 transferred a fiber image into texture blocks, and a projection curve was created based on these texture blocks.…”
Section: Introductionmentioning
confidence: 99%