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Authors: Matthieu Voiry 1 ; Kurosh Madani 2 ; Véronique Amarger 2 and François Houbre 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 detection and characterization in products. This challenging operation is very important since it is directly linked with the produced optical component’s quality. A new scratches and digs defects detection and characterization method exploiting Nomarski microscopy issued imaging has been developed. The items detected using this high-performance approach can correspond to real defects on the structure but some dusts and cleaning marks are detected too. Thus, a classification phase is necessary to complete optical devices diagnosis. In this paper, we describe a data extraction method, which supplies pertinent features from raw Nomarski images issued from industrial process. Then we apply this method to construct a database from real images. Finally we analyse the pertinence of features and the complexity of obtained database by clustering operation using an unsupervised Self Organizing Maps technique. (More)

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Paper citation in several formats:
Voiry, M.; Madani, K.; Amarger, V. and Houbre, F. (2006). Toward Automatic Defects Clustering in Industrial Production Process Combining Optical Detection and Unsupervised Artificial Neural Network Techniques. In Proceedings of the 2nd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2006) - ANNIIP; ISBN 978-972-8865-68-9, SciTePress, pages 25-34. DOI: 10.5220/0001223100250034

@conference{anniip06,
author={Matthieu Voiry. and Kurosh Madani. and Véronique Amarger. and Fran\c{C}ois Houbre.},
title={Toward Automatic Defects Clustering in Industrial Production Process Combining Optical Detection and Unsupervised Artificial Neural Network Techniques},
booktitle={Proceedings of the 2nd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2006) - ANNIIP},
year={2006},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001223100250034},
isbn={978-972-8865-68-9},
}

TY - CONF

JO - Proceedings of the 2nd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2006) - ANNIIP
TI - Toward Automatic Defects Clustering in Industrial Production Process Combining Optical Detection and Unsupervised Artificial Neural Network Techniques
SN - 978-972-8865-68-9
AU - Voiry, M.
AU - Madani, K.
AU - Amarger, V.
AU - Houbre, F.
PY - 2006
SP - 25
EP - 34
DO - 10.5220/0001223100250034
PB - SciTePress

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