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We present a framework for automatic inspection of welding seams based on specular reflections. Therefore, we introduce a novel feature set – called specularity features (SPECs) – describing statistical properties of specular reflections. For classification we use a one-class support-vector approach. The SPECs significantly outperform statistical geometric features and raw pixel intensities, since they capture more complex characteristics and depencies of shape and geometry. We obtain an error rate of 9%, which corresponds to the level of human performance.

WELDING INSPECTION USING NOVEL SPECULARITY FEATURES AND A ONE-CLASS SVM