Abstract
This paper deals with the classification of defects in web type products. Detection of outliers in the training set and objective determination of the defect classes represent an important step towards standardization of the defect classes. An approach that is based on the Generalized Principal Co-ordinate Analysis (GPCA) is described for objective selection of a training set and adaptive class cleaning in a low dimensionality space. This approach is applied on features extracted by modelling the production process by an ergodic process which is characterized by its Autocorrelation Function (ACF). An important advantage of this method is its suitability for large pattern vectors.
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© 1988 Springer-Verlag Berlin Heidelberg
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Eldin, A.T.A., Eldin, H.A.N. (1988). Automated Inspection of Web Type Products in Pseudo-euclidean Spaces. In: Bunke, H., Kübler, O., Stucki, P. (eds) Mustererkennung 1988. Informatik-Fachberichte, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-08895-1_36
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DOI: https://doi.org/10.1007/978-3-662-08895-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-50280-7
Online ISBN: 978-3-662-08895-1
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