Fast image registration algorithm for automated inspection of laser micromachining
V.P. Bessmeltsev, E.D. Bulushev

Full text of article: Russian language.

Abstract:
We investigated the possibility of fast quality control of laser micromachining of surface based on optical measurements. The main objective is to match CAD-model and 3D geometry of the machined surface. We found that standard matching algorithms have either low performance or are ineffective at high noise level or at presence of geometrical distortion. The algorithm based on Ciratefi algorithm, which previously wasn’t used for image registration, was developed. The performance of algorithm was increased by using an iterative search of optimum based on image pyramid. The testing of algorithm on height maps of objects formed by laser methods showed its high accuracy and performance.

Key words:
laser micromachining, optical profilometry, automated inspection, image registration, image matching.

References:

  1. Orazi, L. An automated procedure for material removal rate prediction in laser surface micromanufacturing / L. Orazi, G. Cuccolini, A. Fortunato, G. Tani // The International Journal of Advanced Manufacturing Technology. – 2009. – V. 46(1). – P. 163-171.
  2. Soveja, A. Optimization of TA6V alloy surface laser texturing using an experimental design approach / A. Soveja, E. Cicala, D. Grevey, J. Jouvard // Optics and Lasers in Engineering. – 2008. – V. 46(9). – P. 671-678.
  3. Desai, C.K. Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches / C.K. Desai, A. Shaikh // The International Journal of Advanced Manufacturing Technology. – 2011. – V. 60(9-12). – P. 865-882.
  4. Ciurana, J. Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel / J. Ciurana, G. Arias, T. Ozel // Materials and Manufacturing Processes. – 2009. – V. 24(3). – P. 358-368.
  5. Li, Y. Free-form surface inspection techniques state of the art review / Y. Li, P. Gu // Computer Aided Design. – 2004. – V. 36(13). – P. 1395-1417.
  6. Zitova, B. Image registration methods: a survey // Image and Vision Computing. – 2003. – V. 21(11). – P. 977-1000.
  7. Prieto, F. An Automated Inspection System / F. Prieto, T. Redarce, R. Lepage, P. Boulanger // The International Journal of Advanced Manufacturing Technology. – 2002. – V. 19(12). – P. 917-925.
  8. Wolf, K. An approach to computer-aided quality control based on 3D coordinate metrology / K. Wolf, D. Roller, D. Schäfer // Jounal of Materials Processing Technology. – 2000. – V. 107(1). – P. 96-110.
  9. Besl, P.J. A method for registration of 3-D shapes / P.J. Besl, N.D. McKay // IEEE Transactions on Pattern Anaysis and Machine Intelligence – 1992. – V. 14(2). – P. 239-256.
  10. Rusinkiewicz, S. Efficient Variants of the ICP Algorithm / S. Rusinkiewicz, M. Levoy // 3-D Digital Imaging and Modeling. – 2001. – P. 145-152.
  11. Castro, E. Registration of translated and rotated images using finite Fourier transforms / E. Castro, C. Morandi // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1987. – V. 9(5) – P. 700-703.
  12. Myasnkov, E.V. Determination of parameters of geometric transformation to combine portrait images// E.V. Myasnkov // Computer Optics. – 2007. – V. 31(3). – P. 77-82. – (In Russian).
  13. Chemeris, D.S. Investigation of methods for determining the geometric mismatch of two images for tasks of optical guidance and docking underwater robot / D.S. Chemeris, I.N. Burdinski // Design of engineering and scientific applications in Matlab: Conference proceedings. – Saint-Petersburg, 2011. – P. 465-470. – (In Russian).
  14. Chochia, P.A. Fast correlation matching of quasi-regular images // Information processes. – 2009. – V. 9(3). – P. 117-120. – (In Russian).
  15. Volegov, D.B. Preliminary rough image matching based on extracted lines to build mosaic, super-resolution and reconstruction of 3D scenes / D.B. Volegov, D.V. Urin // Programming. – 2008. – V. 34(5). – P. 47-66. – ISSN 0132-3474. – (In Russian).
  16. Lowe, D.G. Object recognition from local scale-invariant features // Computer Vision. – 1999. – P. 1150-1157.
  17. Briechle, K. Template matching using fast normalized cross correlation // Aerospace, Defense Sensing, Simulation, and Controls. – 2001. – P. 95-102.
  18. Lewis, J.P. Fast Normalized Cross-Correlation // Vision Interface. – 1995. – V. 10(1). – P. 120-123.
  19. Araújo, S.A. Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast / S.A. Ara­újo, H.Y. Kim // IEEE Pacific-Rim Symposium on Image and Video Technology. – 2007. – V. 4872. – P. 100-113.
  20. Tanimoto, S.L. Template matching in pyramids // Computer Graphics and Image Processing. – 1981. – V. 16(4). – P. 356-369.
  21. Vanderbrug, G.J. Two-Stage Template Matching / G.J. Van­derbrug, A. Rosenfeld // Computers, IEEE. Transactions. – 1977. – V. 26(4). – P. 384-393.
  22. Goloshevsky, N. Precision laser system based on complementary scanning principle for dielectric materials microprocessing / N. Goloshevsky, A. Aleshin, S. Baev, V. Bess­meltsev // Fundamentals of Laser Assisted Micro- and Nanotechnologies SPIE. – Ed. by V.P Veiko. – Saint-Petersburg, – 2008. – P. 69850M.1-69850M.9.
  23. Fisher, R. A comparison of algorithms for subpixel peak detection / R.A. Fisher, D.K. Naidu // Image Technology: Advances in Image Processing, Multimedia and Machine Vision. – 1996. – P. 385-404.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: ko@smr.ru; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20