Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Resolution Selection Using Generalized Entropies of Multiresolution Histograms
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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We present an automatic method for the classification of steel quality based on scale-space operations. The carbide distribution of microscopic specimen images is assessed by classifying according to so-called 'degree' and 'type' of the specimen. 'Degree' is represented by features extracted with automatic scale selection, and 'type' information is computed from second-moment descriptors. In combination with a morphological verification scheme, this pattern classifier shares large similarities with current manual techniques. Compared to previous work, the new classification scheme has several advantages: the significant scale of the carbide agglomeration is calculated explicitly, and the method is less sensitive to the variance of spatial connectivity than a morphological approach.