Fabric weave pattern and yarn color recognition and classification using a deep ELM network

被引：12

作者：

Khan, Babar ^{[1
]}

Wang, Zhijie ^{[1
]}

Han, Fang ^{[1
]}

Iqbal, Ather ^{[1
]}

Masood, Rana Javed ^{[2
]}

机构：

[1] Engineering Research Center of Digitized Textile and Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai,201620, China

[2] College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing,210016, China

来源：

Algorithms | 2017年 / 10卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Fault tolerance - Knowledge acquisition - Color - Complex networks - Deep neural networks - Yarn - Cost effectiveness - Weaving - Wool;

D O I：

10.3390/a10040117

中图分类号：

学科分类号：

摘要：

Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation. © 2017 by the authors.

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