Classification of carbide distributions using scale selection and directional distributions

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