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Authors: Matthieu Voiry 1 ; Kurosh Madani 2 ; Véronique Amarger 2 and Joël Bernier 3

Affiliations: 1 Images, Signals, and Intelligent System Laboratory, (LISSI / EA 3956), Paris-XII – Val de Marne University, Senart Institute of Technology; SAGEM REOSC, France ; 2 Images, Signals, and Intelligent System Laboratory, (LISSI / EA 3956), Paris-XII – Val de Marne University, Senart Institute of Technology, France ; 3 SAGEM REOSC, France

Abstract: A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component’s quality. To complete optical devices diagnosis, a classification phase is mandatory since a number of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can however decrease problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evaluate their possible impact on classification tasks performances.

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Paper citation in several formats:
Voiry, M.; Madani, K.; Amarger, V. and Bernier, J. (2007). Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects. In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP; ISBN 978-972-8865-86-3, SciTePress, pages 56-65. DOI: 10.5220/0001635500560065

@conference{anniip07,
author={Matthieu Voiry. and Kurosh Madani. and Véronique Amarger. and Joël Bernier.},
title={Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP},
year={2007},
pages={56-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001635500560065},
isbn={978-972-8865-86-3},
}

TY - CONF

JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP
TI - Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects
SN - 978-972-8865-86-3
AU - Voiry, M.
AU - Madani, K.
AU - Amarger, V.
AU - Bernier, J.
PY - 2007
SP - 56
EP - 65
DO - 10.5220/0001635500560065
PB - SciTePress

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