Inspection of complex surfaces by means of structured light patterns.
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.
Caulier
Yannick
Y
Department Process Integrated Inspection Systems Fraunhofer Institute for Integrated Circuits IIS, D-91058 Erlangen, Germany. yannick.caulier@iis.fraunhofer.de
D000465:Algorithms; D003198:Computer Simulation; D004867:Equipment Design; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D008422:Materials Testing; D008670:Metals / Q000737:chemistry; D008962:Models, Theoretical; D055095:Optics and Photonics; D010363:Pattern Recognition, Automated / Q000379:methods; D020349:Surface Plasmon Resonance / Q000379:methods*; D013499:Surface Properties
D008670:Metals