Automatic Detection of Film Orientation with Support Vector Machines

Authors:
Dane Walsh;Christian W. Omlin
Affiliations:
-;-
Venue:
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Year:
2002

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Abstract

In this paper, we present a technique for automatic orientation detection of film rolls using Support Vector Machines (SVMs). SVMs are able to handle feature spaces of high dimension and automatically choose the most discriminative features for classification. We investigate the use of various kernels, including heavy tailed RBF kernels. Our results show that by using SVMs, an accuracy of 100% can be obtained, while execution time is kept to a mininum.