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논문검색

Study on Recognition Method of Adhering Bars Based on Support Vector Machine

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

저자정보

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

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