Abstract
In machine vision features are the basis for almost any kind of high-level postprocessing such as classification. A new method is developed that uses the inherent flexibility of feature calculation to optimize the features for a certain classification task. By tuning the parameters of the feature calculation the accuracy of a subsequent classification can be significantly improved and the decision boundaries can be simplified. The focus of the methods is on surface inspection problems and the features and classifiers used for these applications.
This work was funded by the EC under grant no. 016429, project DynaVis. It reflects only the authors’ views.
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Eitzinger, C., Gmainer, M., Heidl, W., Lughofer, E. (2008). Increasing Classification Robustness with Adaptive Features. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_43
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DOI: https://doi.org/10.1007/978-3-540-79547-6_43
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