基于目标形态特征的工件自动分割方法

doi:10.13190/j.jbupt.2018-309

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (5): 119-126.doi: 10.13190/j.jbupt.2018-309

基于目标形态特征的工件自动分割方法

逄增治^1,2,3, 史建杰^1,2,3, 尹建芹⁴, 朱利民⁵, 李金屏^1,2,3

1. 济南大学信息科学与工程学院, 济南 250022;
2. 山东省网络环境智能计算技术重点实验室(济南大学), 济南 250022;
3. 山东省"十三五"高校智能信息处理与感知重点实验室, 济南 250022;
4. 北京邮电大学自动化学院, 北京 100876;
5. 滨州渤海活塞有限公司, 山东滨州 256602

收稿日期:2019-01-06 出版日期:2019-10-28 发布日期:2019-11-25
通讯作者: 李金屏(1968-),男,教授,硕士生导师,E-mail:ise_lijp@ujn.edu.cn. E-mail:ise_lijp@ujn.edu.cn
作者简介:逄增治(1995-),男,硕士生.
基金资助:
国家自然科学基金项目（61701192）；山东省重点研发计划项目（2017CXGC0810）；山东省科技重大专项（新兴产业）项目（2015ZDXX0801A03）；山东省教育科学规划"教育招生考试科学研究专设课题"（ZK1337212B008）

Automatic Segmentation of Workpiece Based on Target Morphological Features

PANG Zeng-zhi^1,2,3, SHI Jian-jie^1,2,3, YIN Jian-qin⁴, ZHU Li-min⁵, LI Jin-ping^1,2,3

1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China;
2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing(University of Jinan), Jinan 250022, China;
3. Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13^thFive-Year, Jinan 250022, China;
4. School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China;
5. Binzhou Bohai Piston Company Limited, Shandong Binzhou 250022, China

Received:2019-01-06 Online:2019-10-28 Published:2019-11-25

摘要/Abstract

摘要： 为了使交互式工件分割算法满足实时性的要求，提出了一种将工件形态特征与图像分割算法相结合的工件自动分割方法.利用MeanShift算法分割图像提取目标区域；利用形态学开运算消除目标区域的噪声，进而分离相连的目标区域；对目标区域进行边缘检测，计算完整的工件轮廓信息，然后根据外轮廓的面积确定工件区域；利用工件区域的最小外接矩形在图像中标出前景和背景区域，再利用GrabCut算法分别对前景和背景建立高斯混合模型，然后通过mincut/maxflow算法分割前景与背景区域，最终实现工件目标的提取.实验结果表明，对于制造商提供的样本，该方法分割工件的召回率和准确率分别为94.97%和88.48%，具有较强的实用性和良好的实时性.

关键词: 工件分割, 图像处理, 形态学, 边缘检测, MeanShift算法, GrabCut算法

Abstract: Since many enterprises produce a huge number of workpiece images every day, and the existing interactive workpiece segmentation algorithm can not meet the real-time requirements, a workpiece segmentation method combining workpiece morphological features and image segmentation algorithm is proposed. This method consists of four steps:firstly, the image is segmented by use of MeanShift algorithm to extract the target region; secondly, the noise in the target region is eliminated using morphological open operation, and then the connected target region can be separated; thirdly, the edge detection of the target region is carried out to calculate the complete workpiece contour information, and then the workpiece region is determined according to the size of the outer contour area; fourthly, the foreground and background regions are labeled by using the minimal contour rectangle of the workpiece area and the Gaussian mixture model is established using GrabCut algorithm for foreground and background respectively, then the foreground and background regions can be segmented by use of mincut/maxflow algorithm, and finally the workpiece object can be extracted. The experimental results show that, for the samples provided by the manufacturer, the recall and accuracy of the proposed method are 94.97% and 88.48% respectively, and the method has strong practicability and good real-time performance.

Key words: workpiece segmentation, image processing, morphology, edge detection, MeanShift algorithm, GrabCut algorithm

中图分类号:

TP391

逄增治, 史建杰, 尹建芹, 朱利民, 李金屏. 基于目标形态特征的工件自动分割方法[J]. 北京邮电大学学报, 2019, 42(5): 119-126.

PANG Zeng-zhi, SHI Jian-jie, YIN Jian-qin, ZHU Li-min, LI Jin-ping. Automatic Segmentation of Workpiece Based on Target Morphological Features[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(5): 119-126.

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基于目标形态特征的工件自动分割方法

Automatic Segmentation of Workpiece Based on Target Morphological Features

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