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Image capturing and image content description can be regarded as the two major steps of a computer vision process. This paper focuses on both within the field of specular surface inspection, by generalizing a previously defined stripe-based inspection method to free-form surfaces on the basis of a specific stripe illumination technique and by outlining a general feature-based stripe image characterization approach by means of new theoretical concepts. One major purpose of this paper is to propose a general stripe image interpretation approach on the basis of a three-step procedure: 1) comparison of different image content description techniques, 2) fusion of the most appropriate ones, and 3) selection of the optimal features. It is shown that this approach leads to an increase in the classification rates of more than 2 percent between the initial fused set and the selected one. The new contributions encompass 1) the generalization of a cylindrical specular surface enhancement technique to more complex specular geometries, 2) the generalization of the previously defined stripe image description by using the same number of features for the bright and the dark stripes, and 3) the definition of an optimal, in terms of classification rates and computational costs, stripe feature set.