Classiication of Carbide Distributions Using Scale Selection and Directional Distributions Classiication of Carbide Distributions Using Scale-space Methods Contents 1 Introduction 1 2 Scale Selection Module 2

In the production of high speed steel, the rolling aaects the micro-structure of the steel, which in turn innuences the mechanical properties. Specii-cally, the distribution of carbide is essential, since cracks propagate within the carbide agglomerations. In current quality control, the properties of the steel are assessed manually by comparison with a standard chart, containing representative patterns for each steel class. Interestingly, the standard technique for classifying carbide distributions is two-dimensional, where the rst dimension basically corresponds to scale (\degree" | the size of the largest carbide agglomeration) and the the second dimension basically reeects the directional distribution (\type" | how strongly the net structure of carbide has been stretched). In this paper, we present an automatic method for such classiication based on scale-space operations, in which the size information is measured using recently developed techniques for feature detection with automatic scale selection and the directional information is computed from second-moment descriptors (Lindeberg 1994). Combined with a morphological veriication scheme, a pattern classiier is proposed, which shares large similarities with current manual techniques. Compared to previous work (Wiltschi, Pinz & Hackl 1995), the proposed scheme has the advantage that the signiicant scale of the carbide agglomeration is calculated explicitly, and the method is much less sensitive to the variance of spatial connectivity than a morphological approach. From a theoretical viewpoint, the proposed scheme also has the attractive property that it is based on similar visual-front-end operations as a large class of computer vision modules.