Optical Devices Diagnosis by Neural Classifier Exploiting Invariant Data Representation and Dimensionality Reduction Ability.

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. [ABSTRACT FROM AUTHOR]