New indicators on fabric drape evaluation based on three-dimensional model

Yu, Zhicai; Zhong, Yueqi; Gong, Rong; Xie, Haoyang

doi:10.1177/0040517519888669

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The samples were then cut into circular disks with a diameter of 240 mm and conditioned for 24 hours at a constant temperature (20 AE2 C) and humidity (65 AE 4%). We used our inhouse device, 7 which consists of a supporting disk with four RGB-D cameras 20 surrounding it and a T-shaped checkerboard for camera calibration 21 to scan each fabric sample approximately 100 times. The RGB-D cameras can provide an RGB image, infrared (IR) image, and depth map simultaneously based on the optical coding system.…”

Section: Data Preparationmentioning

confidence: 99%

“…A more detailed procedure for generating the meshes is described in our previous work. 7,22 This process generated a total of 5160 drape meshes, which need to be aligned along the þY direction before the feature extraction process can be performed. The detailed procedure follows.…”

Section: Data Preparationmentioning

confidence: 99%

“…Stylios and Zhu 6 manually defined a set of feature vectors based on computer vision to characterize the drape of fabrics as an aesthetic property. Yu et al 7 defined the TDA (total drape angle) and DADF (drape angle distribution function) from the statistical analysis of drape angles in a triangular mesh. In addition, Lin and Stylios 8 developed an analytical model based on applied mechanics, and focused on the amplitudes and wavelengths of the buckling behaviors associated with fabric drape.…”

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confidence: 99%

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Characterization of woven fabric drape based on neural networks

Han

Luo

Wang

et al. 2023

Textile Research Journal

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Previous research on fabric drape has not provided an objective and comprehensive characterization of drape characteristics. In light of this, we proposed an approach that utilizes a neural network-based framework for characterizing the umbrella drape of woven fabrics. Fabric drapes with the same macro-level mechanical characteristics can be categorized together, thereby establishing objective classification criteria. Our method involved feature extraction and classification from drape images/point clouds via neural networks, namely ResNet18 and the deep graph convolutional neural network (DGCNN). We assessed the effectiveness of both networks through supervised learning and selected the best candidate to distinguish/retrieve drape styles from unlabeled data. Moreover, a sketch down-sampling (SDS) tailored to accurately represent point clouds of umbrella-shaped drapes was devised. In all, 5160 drape meshes were collected by RGB-D cameras and GeomagicTM. Two neural networks were trained for 30 epochs using stochastic gradient descent with a momentum of 0.9. The learning rate was set to 0.1 for ResNet18 and 0.001 for the DGCNN. Experimental results demonstrated that the DGCNN coupled with the SDS method was the optimal feature extraction solution for woven fabric drapes, given that the accuracy reached 97% with the coefficient of variation of 7%. Therefore, our approach offered an objective and precise quantification of fabric drape, which provided a possible downstream application for searching fabrics based on drape similarity.

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Section: Data Preparationmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

mentioning

confidence: 99%

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Characterization of woven fabric drape based on neural networks

Han

Luo

Wang

et al. 2023

Textile Research Journal

Self Cite

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“…4 Yu et al proposed the total drape angle index, which is based on 3D fabric drapability to characterize the drapability of woven fabrics. 5 Azmat et al proposed a fabric drape height index, which can be used to evaluate fabric drape objectively. 6 Zhang et al used image processing technology to evaluate the fabric shape retention by calculating the convex area of the four side cross-section image of the fabric.…”

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confidence: 99%

Effect of test parameters on the stability of fabric shape retention indexes increase recovery testing

Tang

Wang

Gao

et al. 2021

Textile Research Journal

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Fabric shape retention is a crucial property that impacts the durability and usability of fabric and which needs a convenient and accurate evaluation method. In the previous research, the automated crease recovery testing method was used to obtain fabric crease recovery information and evaluate the property of shape retention. Based on the previous research, an orthogonal test method was adopted to investigate the effect of different test parameters on the stability of shape retention detection in this paper. First, three factors, that of sample size, pressure, and pressure time, and three different levels of each factor were determined by the L9(34) orthogonal test table. Next, the fabric shape retention indexes were detected by nine different test schemes, and the comprehensive score of shape retention index standard deviation was obtained as the evaluation criterion of the orthogonal test results. Finally, the optimal test scheme was determined by visual analysis and variance analysis. The results show that the sample size has a certain effect on the stability of shape retention indexes, while the pressure time and the pressure have no significant influence. The optimal test scheme is that the sample size is 30 cm × 30 cm, the pressure time is 60 s, and the pressure is 40 N. The test result measured by the optimal test parameters reveals excellent stability when the vertex angle standard deviation is 2.0°, the height standard deviation is 0.06 cm, and the shape retention area standard deviation is 0.16 cm2. This paper provides an experimental basis for improving the accuracy of fabric shape retention evaluation method.

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