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Structured light camera calibration

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Structured light camera which is being designed with the joined effort of Institute of Radioelectronics and Institute of Optoelectronics (both being large units of the Warsaw University of Technology within the Faculty of Electronics and Information Technology) combines various hardware and software contemporary technologies. In hardware it is integration of a high speed stripe projector and a stripe camera together with a standard high definition video camera. In software it is supported by sophisticated calibration techniques which enable development of advanced application such as real time 3D viewer of moving objects with the free viewpoint or 3D modeller for still objects.
Rocznik
Strony
23--38
Opis fizyczny
Bibliogr. 45 poz., rys., il., wykr.
Twórcy
autor
  • Faculty of Electronics and Information Technology, Warsaw University of Technology, 1 Pl. Politechniki, 00-661, Warsaw, Poland
autor
  • Faculty of Electronics and Information Technology, Warsaw University of Technology, 1 Pl. Politechniki, 00-661, Warsaw, Poland
  • Faculty of Electronics and Information Technology, Warsaw University of Technology, 1 Pl. Politechniki, 00-661, Warsaw, Poland
Bibliografia
  • 1. S. J Koppal, S. Yamazaki, S. G. Narasimhan, „Exploiting dlp illumination dithering for reconstruction and photography of high-speed scenes”, Int. J. Comput. Vision 96, 125-144, (2012).
  • 2. V. C. Paquit, K. W. Tobin, J. R. Price, and F. Meriaudeau, „3D and multispectral imaging for subcutaneous veins detection”, Opt. Express 17, 11360-11365 (2009).
  • 3. W. Gao, L. Wang, and Z. Y. Hu, „A flexible method for structured light system calibration”, Opt. Eng. 47, 083602, (2008).
  • 4. Q. A. Li, M. Biswas, M. R. Pickering, M. R. Frater, „Accurate depth estimation using structured light and passive stereo disparity estimation”, IEEE Int. Conf. Image Process., Brussels, pp. 969-972, (2011).
  • 5. http://en.wikipedia.org/wiki/Structured_light
  • 6. G. Wiora, „High resolution measurement of phase-shift amplitude and numeric object phase calculation”, Proc. SPIE 4117, 289-299 (2000).
  • 7. Microsoft Kinect. Available online: http://www.xbox.com/en-us/kinect/ (accessed on).
  • 8. K. Khoshelham, S. O. Elberink, „Accuracy and resolution of kinect depth data for indoor mapping applications”, Sensors 12, 1437–1454 (2012).
  • 9. J. Ghring, „Dense 3-d surface acquisition by structured light using off-the-shelf components”, Proc. SPIE Videometrics and Optical Methods for 3D Shape Measurement 4309, 220-231 (2001).
  • 10. E. Horn and N. Kiryati, „Toward optimal structured light patterns”, Image Vision Comput. 17, 87-97 (1999).
  • 11. E. Trucco, R. B. Fisher, A. W. Fitzgibbon, and D. K. Naidu, „Calibration, data consistency and model acquisition with laser stripers”, Int. J. Computer Integrated Manufacturing 11, 293-310 (1998).
  • 12. R. J. Valkenburg and A. M. McIvor, „Accurate 3D measurement using a structured light system”, Image Vision Comput. 16, 99-110 (1998).
  • 13. J. L. Posdamer and M. D. Altschuler, „Surface measurement by space-encoded projected beam systems”,Comput.Graph. Image Process. 18, 1-17 (1982).
  • 14. Z. J. Geng, „Rainbow 3-dimensional camera: New concept of high-speed 3-dimensional vision systems”, Opt. Eng. 35, 376-383 (1996).
  • 15. C. Wust and D. W. Capson, „Surface profile measurement using colour fringe projection”, Mach. Vision Appl. 4, 193-203, (1991).
  • 16. T. Pajdla, „Bcrf - binary-coded illumination range finder reimplementation”, Technical report KUL/ESAT/MI2/9502, Katholieke Universiteit Leuven, ESAT, Leuven, 1995.
  • 17. P. Lavoie, D. Ionescu, and E. Petriu, „A high precision 3D object reconstruction method using a colour coded grid and nurbs”, Proc. Int. Conf. Image Analysis and Processing, pp. 370-375, Venice, 1999.
  • 18. J. Tajima and M. Iwakawa, „3-D data acquisition by rainbow range finder”, Proc. IEEE Int. Conf. Pattern Recogn., pp. 309-313, Atlantic City, 1990.
  • 19. D. Bergmann, „New approach for automatic surface reconstruction with coded light”, Proc. SPIE Remote Sensing and Reconstruction for Three-Dimensional Objects and Scenes, Vol. 2572, pp. 2-9, San Diego, 1995.
  • 20. M. Ito and A. Ishii, „A three-level checkerboard pattern (TCP) projection method for curved surface measurement”, Pattern Recogn. 28, 27-40 (1995).
  • 21. S. Kiyasu, H. Hoshino, K. Yano, and S. Fujimura, „Measurement of the 3-D shape of specular polyhedrons using an m-array coded light source”, IEEE T. Instrumentation and Measurement 44, 775-778 (1995).
  • 22. S. Inokuchi, K. Sato, and F. Matsuda, „Range-imaging for 3-D object recognition”, Int. Conf. Pattern Recogn., pp. 806-808, Montreal, 1984.
  • 23. W. Krattenthaler, K. J. Mayer, and H. P. Duwe, „3D-surface measurement with coded light approach”, Proc. 17th Meeting of the Austrian Association for Pattern Recognition on Image Analysis and Synthesis, Vol. 12, pp. 103-114, 1995.
  • 24. K. Sato, „Range imaging based on moving pattern light and spatio-temporal matched filter”, IEEE Int. Conf. Image Process. 1, pp. 33-36, Lausanne, 1996.
  • 25. T. Monks and J. Carter, „Improved stripe matching for colour encoded structured light”, 5th Int. Conf. Computer Anal. Images and Patterns, pp. 476-485, Budapest, 1993.
  • 26. E. M. Petriu, Z. Sakr, S. H. J. W., and A. Moica, „Object recognition using pseudo-random colour encoded structured light”, Proc. IEEE 17th IEEE Instrumentation and Measurement Technology Conference, Vol. 3, pp.1237-1241, Baltimore, 2000.
  • 27. H. Morita, K. Yajima, and S. Sakata, „Reconstruction of surfaces of 3-D objects by m-array pattern projection method”, in IEEE Int. Conf. Comput. Vision, pp. 468-473, Tampa, 1988.
  • 28. J. Salvi, J. Pages, and J. Batlle, „Pattern codification strategies in structured light systems”, Pattern Recogn. 37, 827-849 (2004).
  • 29. C. Chen, Y. Hung, C. Chiang, and J. Wu, „Range data acquisition using colour structured lighting and stereo vision”, Image Vision Comput. 15, 445-456 (1997).
  • 30. J. Salvi, J. Batlle, and E. Mouaddib, „A robust-coded pattern projection for dynamic 3D scene measurement”, Int. J. Pattern Recogn. Lett. 19, 1055–1065 (1998).
  • 31. P. Griffin, L. Narasimhan, and S. Yee, „Generation of uniquely encoded light patterns for range data acquisition”,Pattern Recogn. 25, 609-616 (1992).
  • 32. I. Ishii, K. Yamamoto, K. Doi, and T. Tsuji, „High-speed 3D image acquisition using coded structured light projection”, IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 925-930, San Diego, 2007.
  • 33. L. Zhang, B. Curless, and S. M. Seitz, „Rapid shape acquisition using colour structured light and multi-pass dynamic programming”, Int. Symp. on 3D Data Processing Visualization and Transmission, Padova, 2002.
  • 34. P. Fechteler and P. Eisert, „Adaptive colour classification for structured light systems”, IET J. Comput. Vision 3, 49-59, 2009.
  • 35. J. Pages, J. Salvi, C. Collewet, and J. Forest, „Optimised De Bruijn patterns for one-shot shape acquisition”,Image Vision Comput. 23, 707-720 (2005).
  • 36. D. Caspi, N. Kiryati, and J. Shamir, „Range imaging with adaptive colour structured light”, Pattern Anal. Machine Intel. 20, 470-480 (1998).
  • 37. K. L. Boyer and A. C. Kak, „Colour-encoded structured light for rapid active ranging”, IEEE T. Pattern Analy. and Machine Intel. 9, 14–28 (1987).
  • 38. H. Fredricksen, „A survey of full length nonlinear shift register cycle algorithms”, Society of Industrial and Applied Mathematics Review 24, 195-221 (1982).
  • 39. P. Vuylsteke and A. Oosterlinck, „Range image acquisition with a single binary-encoded light pattern”, IEEE T. Pattern Anal. and Machine Intel. 12, 148-163 (1990).
  • 40. O. Hall-Holt and S. Rusinkiewicz, „Stripe boundary codes for real-time structured-light range scanning of moving objects”, in 8th IEEE Int. Conf. Comput. Vision, pp. 359-366, Vancouver, 2001.
  • 41. Z. Zhang, „A Flexible New Technique for Camera Calibration”, IEEE T. Pattern Anal. and Machine Intel. 22, 1330–1334 (2000).
  • 42. C. Harris and M. Stephens, „A combined corner and edge detector”, Proc. 4th Alvey Vision Conf., Manchester, 1998.
  • 43. O. Faugeras,Three-Dimensional Computer Vision, edited by MIT Press, Cambridge, 1993.
  • 44. M. Pharr and G. Humphreys, Physically Based Rendering, edited by Morgan-Kauffman, Burlington, 2004.
  • 45. G. Sansoni, S. Lazzari, S. Peli, and F. Docchio, „3d imager for dimensional gauging of industrial workpieces: state of the art of the development of a robust and versatile system”, Int. Conf. Recent Advances in 3-D Digital Imaging and Modeling, pp. 1926, Ottawa, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAD-0033-0002
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