人工智能
人工神经网络
计算机科学
卷积神经网络
模式识别(心理学)
机织物
多边形网格
点云
计算机视觉
工程类
计算机图形学(图像)
运营管理
作者
Woo-Sup Han,Weihao Luo,Xin Wang,Yueqi Zhong
标识
DOI:10.1177/00405175231188534
摘要
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 Geomagic TM . 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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