Christian Eitzinger, Manfred Gmainer, Wolfgang Heidl, Edwin Lughofer,
"Increasing Classification Robustness with Adaptive Features"
, in A. Gasteratos and M. Vincze and J.K. Tsotsos: Proc. International Conference on Computer Vision Systems 2008, Serie Lecture Notes, Vol. 5008, Springer, Berlin, Seite(n) 445--453, 2008
Increasing Classification Robustness with Adaptive Features
Sprache des Titels:
Proc. International Conference on Computer Vision Systems 2008
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.