Classiication of Carbide Distributions Using Scale Selection and Directional Distributions

Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Abstract We present an automatic method for the classii-cation 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 veriication scheme, this pattern classi-er shares large similarities with current manual techniques. Compared to previous work, the new classii-cation scheme has several advantages: The signiicant scale of the carbide agglomeration is calculated explicitly , and the method is less sensitive to the variance of spatial connectivity than a morphological approach.