Automated Pattern Recognition and Defect Inspection System
Packaging appearance is extremely important in cigarette manufacturing. Typically, there are two types of cigarette packaging defects: (1) cigarette laying defects such as incorrect cigarette numbers and irregular layout; (2) tin paper handle defects such as folded paper handles. In this paper, an automated vision-based defect inspection system is designed for cigarettes packaged in tin containers. The first type of defects is inspected by counting the number of cigarettes in a tin container. First kmeans clustering is performed to segment cigarette regions. After noise filtering, valid cigarette regions are identified by estimating individual cigarette area using linear regression. The k clustering centers and area estimation function are learned off-line on training images. The second kind of defect is detected by checking the segmented paper handle region. Experimental results on 500 test images demonstrate the effectiveness of the proposed inspection system. The proposed method also contributes to the general detection and classification system such as identifying mitosis in early diagnosis of cervical cancer.
Yue Cui Jesse S. Jin Suhuai Luo Mira Park Sherlock S. L. Au
School of Design, Communication and IT, the University of Newcastle, Callaghan, 2308, Australia Global Advanced Vision Pty Ltd, Room 1102, R&D Centre, Zhonglou, Changzhou, P.R. China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
768-773
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）