A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION

Navid Rabbani, Mohammad Alamdari, Mohammad Rohollah Yazdani, Farhad Imanpour

2009

Abstract

This paper presents a novel approach for detection and classification of steel sheet defects. A Defects database with enough samples and good imaging conditions introduced. A set of new features proposed to extract the appropriate textural characteristics from defects images. This is followed by the selection of important features using SFFS algorithm. Modifications to SFFS feature selection method were presented to achieve the real-time needs of research. The proposed scheme decrease computational complexity in cost of little decreasing of classification accuracy.

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Paper Citation


in Harvard Style

Rabbani N., Alamdari M., Yazdani M. and Imanpour F. (2009). A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 250-253. DOI: 10.5220/0001784702500253

in Bibtex Style

@conference{visapp09,
author={Navid Rabbani and Mohammad Alamdari and Mohammad Rohollah Yazdani and Farhad Imanpour},
title={A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION },
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={250-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001784702500253},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - A NOVEL FEATURE EXTRACTION AND SELECTION METHOD FOR STEEL SHEET DEFECTS CLASSIFICATION
SN - 978-989-8111-69-2
AU - Rabbani N.
AU - Alamdari M.
AU - Yazdani M.
AU - Imanpour F.
PY - 2009
SP - 250
EP - 253
DO - 10.5220/0001784702500253