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.
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Walsh, D., Omlin, C. (2002). Automatic Detection of Film Orientation with Support Vector Machines. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_5
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DOI: https://doi.org/10.1007/3-540-48035-8_5
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