Optical devices diagnosis by neural classifier exploiting invariant data representation and dimensionality reduction ability

Authors:
Matthieu Voiry;Kurosh Madani;Véronique Amarger;Joël Bernier
Affiliations:
Images, Signals, and Intelligent System Laboratory, Paris-XII - Val de Marne University, Senart Institute of Technology, Lieusaint, France and SAGEM REOSC, Saint Pierre du Perray, France;Images, Signals, and Intelligent System Laboratory, Paris-XII - Val de Marne University, Senart Institute of Technology, Lieusaint, France;Images, Signals, and Intelligent System Laboratory, Paris-XII - Val de Marne University, Senart Institute of Technology, Lieusaint, France;SAGEM REOSC, Saint Pierre du Perray, France
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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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. In order to automate this repetitive and difficult task, microscopy based inspection system is aimed. After a defects detection phase, a classification phase is mandatory to complete optical devices diagnosis because a number of correctable defects are usually present beside the potential "abiding" ones. In this paper is proposed a processing sequence, which permits to extract pertinent low-dimensional defects features from raw microscopy issued image. The described approach is validated by studying MLP neural network based classification on real industrial data using obtained defects features.