원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.6 No.2
2012.04
pp.161-166
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
1. Introduction
2. Defects Detection and Classification
3. Experimental Results
4. Conclusions
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보