Earticle

초록

영어

This paper describes a framework for steel surface defects detection and classification. We use SIFT for defects regions detection and features extraction for the following SVM classification. This approach can generate many feature points for training the classifier from a few images. We also propose a voting strategy for the final decision that handles the problem of multiple outputs of a given input image with a specific defect type. In addition, this approach improves the classification performance. Experimental results demonstrate the effectiveness of the proposed method on steel surface defects detection and classification.

목차

Abstract
 1. Introduction
 2. Defects Detection and Classification
 3. Experimental Results
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• B. Suvdaa [ School of Info. Tech., National University of Mongolia, Mongolia ]
• J. Ahn [ Div. of Computer & Media Engineering, Kangnam University, Korea ]
• J. Ko [ Dep. of Computer Engineering, Kumoh National Institute of Technology, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605