Earticle

초록

영어

It is difficult to track, count and separate the moving bars at a high speed on production line for their overlap and accumulation. Therefore, it is necessary to establish a reliable, practical recognition and segmentation mechanism for the adhered bars. A new solution to the problem of bars adhesion is proposed: a support vector machine is constructed to recognize the adhesion type of bars by the feature vectors of training samples. The geometric feature values and moment feature values based on Blob regions in images are extracted, which is the input feature vector of support vector machine. The trained classifier is used for identifying the adhesion type of bars in images. Finally, classification and recognition is carried by support vector machine. The experimental results show that the recognition accuracy based RBF kernel achieves 100%. The method is feasible and effective for the recognition and segmentation of the adhered bars.

목차

Abstract
 1. Introduction
 2. Method
 3. Support Vector Machine
 4. Experimental Analysis
  4.1. Feature Extraction
  4.2 Sample Set
  4.3 Classification and Recognition Results
 5. Conclusion
 References

키워드

저자

• Guohua Liu [ School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China ] Corresponding author
• Liangyu Li [ School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China ]
• Bingle Liu [ School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605