This paper addresses the generalization of a surface inspection methodology developed within an industrial context for the characterization of specular cylindrical surfaces. The principle relies on the interpretation of a stripe pattern, obtained after projecting a structured light onto the surface to be inspected. The main objective of this paper is to apply this technique to a broader range of surface geometries and types, i.e. to free-form rough and free-form specular shapes. One major purpose of this paper is to propose a general free-form stripe image interpretation approach on the basis of a four step procedure: (i) comparison of different feature-based image content description techniques, (ii) determination of optimal feature sub-groups, (iii) fusion of the most appropriate ones, and (iv) selection of the optimal features. The first part of this paper is dedicated to the general problem statement with the definition of different image data sets that correspond to various types of free-form rough and specular shapes recorded with a structured illumination. The second part deals with the definition and optimization of the most appropriate pattern recognition process. It is shown that this approach leads to an increase in the classification rates of more than 2 % between the initial fused set and the selected one. Then, it is demonstrated that with approximately a fourth of the initial features, similar high classification rates of free-form surfaces can be obtained.
In optical nondestructive testing, a novel solution is presented for fault detection based on the interpretation of fringe images. These images can be acquired using different optical methods, such as structured lighting or interferometry. We propose a set of eight special features adapted to the problem of surface inspection using structured illumination. These characteristics are combined with six further features specially developed for the classification of faults using interferometric images. We apply two kinds of decision rules: the Bayesian and the nearest neighbor classifiers. The proposed features are evaluated using a noisy and a noise-free image data set. All patterns were obtained by means of structured lighting. Concerning the noisy data set, we obtain better classification rates when all the 14 features are used in combination with a one-nearest-neighbor classifier. In case of a noise-free data set, we show that similar classification rates are obtained when the 14 features or only the 8 specific features are involved. The methods described are designed to address a broad range of optical nondestructive applications involving the interpretation and classification of fringe patterns
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 stripebased 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.