Fourier-Based Inspection of Free-Form Reflective Surfaces

A general free-form surface inspection approach relying on the projection of a structured light pattern and the interpretation of the generated stripe structures by means of Fourier-based features is proposed in this paper. The major concerns of this paper are the determination of various refrence sets of stripe patterns, and the detailed investigation on the subset of Fourier features that best characterizes free-form bright/dark structures. In order to tackle the inspection problem with a general approach, a first part of this paper is dedicated to the definition of different image data sets that correspond to various types of free-form specular shapes recorded with a structured illumination. A second part deals with the optimization of the most appropriate pattern recognition process. The optimization is dedicated to the use of different pattern arrangements, and the evaluation of different Fourier feature subsets. It is shown that with only 10 Fourier features and a certain pattern arrangement, high classification rates of free-form surfaces can be obtained.

[1]  Pedro E. López-de-Teruel,et al.  Nonlinear kernel-based statistical pattern analysis , 2001, IEEE Trans. Neural Networks.

[2]  Ari Visa,et al.  Fourier-Based Object Description in Defect Image Retrieval , 2006, Machine Vision and Applications.

[3]  Markus C. Knauer,et al.  Phase measuring deflectometry: a new approach to measure specular free-form surfaces , 2004, SPIE Photonics Europe.

[4]  Sören Kammel,et al.  Deflektometrische Untersuchung spiegelnd reflektierender Freiformflächen , 2005 .

[5]  M. Takeda,et al.  Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry , 1982 .

[6]  Franz Pernkopf,et al.  Visual Inspection of Machined Metallic High-Precision Surfaces , 2002, EURASIP J. Adv. Signal Process..

[7]  Matthew Goldstein,et al.  k-Nearest Neighbor Classification , 1972 .

[8]  Shlomo Geva,et al.  Adaptive nearest neighbor pattern classification , 1991, IEEE Trans. Neural Networks.

[9]  Tie Jun Cui,et al.  Fast algorithms for large-scale periodic structures using subentire domain basis functions , 2005, IEEE Transactions on Antennas and Propagation.

[10]  Octavia I. Camps,et al.  Weighted Parzen Windows for Pattern Classification , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[12]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Wolfgang Osten,et al.  Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors , 2000, Electronic Imaging.

[14]  Xide Li.  Wavelet transform for detection of partial fringe patterns induced by defects in nondestructive testing of holographic interferometry and electronic speckle pattern interferometry , 2000 .

[15]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[16]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[17]  Cem Ünsalan,et al.  PATTERN RECOGNITION METHODS FOR TEXTURE ANALYSIS CASE STUDY: STEEL SURFACE CLASSIFICATION , 1998 .

[18]  Du-Ming Tsai,et al.  Automated surface inspection for statistical textures , 2003, Image Vis. Comput..

[19]  Anand Asundi,et al.  Fault detection by interferometric fringe pattern analysis using windowed Fourier transform , 2005 .

[20]  C.-H. Grunditz,et al.  Constructing a neural system for surface inspection , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[21]  Gian Luca Foresti,et al.  Generalized neural trees for pattern classification , 2002, IEEE Trans. Neural Networks.

[22]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[23]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[24]  Salah Bourennane,et al.  Specific features for the analysis of fringe images , 2008 .

[25]  Sören Kammel.  Deflektometrie zur Qualitätsprüfung spiegelnd reflektierender Oberflächen (Deflectometry for Quality Control of Specular Surfaces) , 2003 .

[26]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[27]  Ian Witten,et al.  Data Mining , 2000 .

[28]  Salah Bourennane,et al.  New Structured Illumination Technique for the Inspection of High-Reflective Surfaces: Application for the Detection of Structural Defects without any Calibration Procedures , 2008, EURASIP J. Image Video Process..

[29]  W.H. Tang,et al.  A Probabilistic Classifier for Transformer Dissolved Gas Analysis With a Particle Swarm Optimizer , 2008, IEEE Transactions on Power Delivery.

[30]  Matthew Goldstein,et al.  Kn -nearest Neighbor Classification , 1972, IEEE Trans. Inf. Theory.

[31]  Seah Hock Soon,et al.  Fault detection from temporal unusualness in fringe patterns , 2006 .