[1]沈海洋[],陈瑞[],焦良葆[]. 一种基于图像配准的钢绳芯抽动识别技术[J].计算机技术与发展,2017,27(10):201-204.
　SHEN Hai-yang[],CHEN Rui[],JIAO Liang-bao[]. A Technique of Recognition on Twitch of Steel Cord Based on Image Registration[J].,2017,27(10):201-204.
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 一种基于图像配准的钢绳芯抽动识别技术()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
201-204
栏目:
应用开发研究
出版日期:
2017-10-10

文章信息/Info

Title:
 A Technique of Recognition on Twitch of Steel Cord Based on Image Registration
文章编号:
1673-629X（2017)10-0201-04
作者:
 沈海洋[1] ; 陈瑞[2] ; 焦良葆[3]
 1.南京邮电大学 通信与信息工程学院；2.南京工程学院 通信工程学院；3.南京工程学院 康尼研究院
Author(s):
 SHEN Hai-yang[1] ; CHEN Rui[2] ; JIAO Liang-bao[3]
关键词:
 ; X光图像; 图像配准; SURF特征点; RANSAC算法; 抽动检测
Keywords:
 ; X-ray image; image registration; SURF point; RANSAC algorithm; shift detection
分类号:
TP301
文献标志码:
A
摘要:
 针对钢丝绳芯输送带X光成像的图像特征,提出了一种基于图像配准的钢丝绳芯抽动识别方法.该方法在研究图像配准和SURF特征的原理及其一般实现方法的基础上,提取参考图像和待配准图像的SURF特征点对,并利用随机采样一致算法(RANSAC算法)对特征点对进行过滤,以去除误匹配点对;再以获得的匹配点对作为源数据计算图像的变换参数,并对参考图像进行几何变换;将已变换的基准图像和待识别图像进行作差处理,提取差值较大的区域并进行区域识别,即可最终确定钢绳芯抽动状况.为验证所提出方法的有效性和可行性,进行了对比验证实验.实验结果表明,相对于通过检测接头来识别钢丝绳芯抽动的常规方法,所提出的方法更具有普适性,同时还能够避免接头检测引入的额外误差,可较好地识别钢芯的抽动情况.
Abstract:
 Regarding the characteristics of the X-ray image of the steel cord conveyor belt,a method of detecting the twitch of the steel cord based on image registration is proposed,in which the SURF feature points of the current image and reference image are extracted based on principles and general means of image registration and SURF feature,and then the error matched points are removed with ran-dom sample consensus algorithm ( RANSAC) . The image transforming parameters could be gained according to the matched points as the source data,and the reference image is in a geographical transform. The difference matrix between the reference image and current image is gained,and the twitching corresponding to the larger-value area in the difference matrix could be detected and the shifting situation be ascertained. The contrast experiment has been conducted for verification on its feasibility and effectiveness. Experimental results show that compared with the normal detection approach,it has more applicability and has decreased the additional errors and detected more effec-tively.

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更新日期/Last Update: 2017-11-24