基于多特征融合图像分析技术的羊毛与羊绒鉴别

doi:10.13475/j.fzxb.20180403907

纺织学报 ›› 2019, Vol. 40 ›› Issue (03): 146-152.doi: 10.13475/j.fzxb.20180403907

基于多特征融合图像分析技术的羊毛与羊绒鉴别

邢文宇¹, 邓娜¹, 辛斌杰²(), 于晨²

1.上海工程技术大学电子电气工程学院, 上海 201620
2.上海工程技术大学服装学院, 上海 201620

收稿日期:2018-04-17 修回日期:2018-11-29 出版日期:2019-03-15 发布日期:2019-03-15
通讯作者: 辛斌杰
作者简介:邢文宇(1995—),男,硕士生。主要研究方向为纤维图像鉴别。
基金资助:
上海市自然科学基金资助项目(18ZR1416600)

Identification of wool and cashmere based on multi-feature fusion image analysis technology

XING Wenyu¹, DENG Na¹, XIN Binjie²(), YU Chen²

1. School of Electric and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2. Fashion College, Shanghai University of Engineering Science, Shanghai 201620, China

Received:2018-04-17 Revised:2018-11-29 Online:2019-03-15 Published:2019-03-15
Contact: XIN Binjie

摘要/Abstract

摘要：

为快速准确地鉴别羊毛与羊绒,提出一种基于多特征融合的鉴别方法。首先利用光学显微镜及数码相机对羊毛与羊绒纤维进行图像采集,然后分别采用2种类型的预处理操作得到单根纤维图像与去除背景的纤维二值图像;其次通过灰度共生矩阵算法提取第1类预处理后羊毛与羊绒纤维图像的纹理特征参数,基于中轴线算法提取第2类预处理后纤维图像的直径形态特征参数;最后将纹理及形态特征参数融合成多维数组并通过K均值算法进行聚类识别。实验结果显示,与传统利用单一纤维特征提取算法进行识别的方法相比,该算法平均识别率可达到95.25%,识别率较高,可用于羊毛与羊绒纤维的自动分类识别。

关键词: 羊毛, 羊绒, 多特征融合, 纤维鉴别, 灰度共生矩阵, 中轴线算法, K均值算法

Abstract:

For rapid identification of wool and cashmere, a method based on the multi-feature fusion for the fiber identification was proposed. Firstly, the images of wool and cashmere fibers were captured by an optical microscope and a digital camera. Secondly, two kinds of preprocessing operations were carried out respectively, and the binary images of single fiber image and background free fiber were obtained. Then, the texture parameters of the first kind of cashmere and wool fiber images were extracted by the gray level co-occurrence matrix algorithm and the diameter parameters of the second kinds of fiber images were extracted based on the central axis algorithm. Finally, the texture and morphological feature parameters were fused into multidimensional array and the clustering analysis was carried out by the K-means algorithm. The experimental results show that the average identification rate of the algorithm proposed can reach 95.25%. Compared with the conventional single fiber feature extraction algorithm, the recognition rate is high, which confirmed that this method can be used for automatic classification and identification of cashmere and wool fibers.

Key words: wool, cashmere, multi-feature fusion, fiber identification, gray level co-occurrence matrix, central axis method, K-means algorithm

中图分类号:

TS102.3

邢文宇, 邓娜, 辛斌杰, 于晨. 基于多特征融合图像分析技术的羊毛与羊绒鉴别[J]. 纺织学报, 2019, 40(03): 146-152.

XING Wenyu, DENG Na, XIN Binjie, YU Chen. Identification of wool and cashmere based on multi-feature fusion image analysis technology[J]. Journal of Textile Research, 2019, 40(03): 146-152.

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羊绒与羊毛根数比	根数识别率/%
4∶6	92.3
5∶5	91.5
6∶4	92.0

样本名称	样本数量/ 根	识别正确数量/根	根数识别率/%
羊绒	200	189	94.50
羊毛	200	192	96.00
合计	400	381	95.25

基于多特征融合图像分析技术的羊毛与羊绒鉴别

Identification of wool and cashmere based on multi-feature fusion image analysis technology

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