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Journal of Sensors
Volume 2013, Article ID 832963, 16 pages
http://dx.doi.org/10.1155/2013/832963
Research Article

Rapid 3D Modeling and Parts Recognition on Automotive Vehicles Using a Network of RGB-D Sensors for Robot Guidance

Faculty of Engineering, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada K1N 6N5

Received 15 March 2013; Revised 9 July 2013; Accepted 12 July 2013

Academic Editor: Lei Wang

Copyright © 2013 Alberto Chávez-Aragón et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Zhou, J. Tong, L. Liu, Z. Pan, and H. Yan, “Scanning 3D full human bodies using kinects,” IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 4, pp. 643–650, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Maimone and H. Fuchs, “Encumbrance-free telepresence system with real-time 3D capture and display using commodity depth cameras,” in Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR '11), pp. 137–146, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. P. J. Noonan, T. F. Cootes, W. A. Hallett, and R. Hinz, “The design and initial calibration of an optical tracking system using the microsoft kinect,” in Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC '11), pp. 3614–3617, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Rakprayoon, M. Ruchanurucks, and A. Coundoul, “Kinect-based obstacle detection for manipulator,” in Proceedings of the IEEEE/SICE International Symposium on System Integration (SII '11), pp. 68–73, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Berger, K. Ruhl, M. Albers et al., “The capturing of turbulent gas flows using multiple kinects,” in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV '11), pp. 1108–1113, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Smisek, M. Jancosek, and T. Pajdla, “3D with kinect,” in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV '11), pp. 1154–1160, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-S. Park, S.-W. Kim, D. Kim, and S.-R. Oh, “Comparison of plane extraction performance using laser scanner and kinect,” in Proceedings of the 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI '11), pp. 153–155, Seoul, Korea, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Burrus, “Demo software to visualize, calibrate and process kinect cameras output,” 2012, http://nicolas.burrus.name/index.php/Research/KinectRgbDemoV6.
  9. M. Gaffney, “Kinect/3D scanner calibration pattern,” 2011, http://www.thingiverse.com/thing:7793.
  10. K. Berger, K. Ruhl, Y. Schroeder, C. Brummer, A. Scholz, and M. Magnor, “Markerless motion capture using multiple color-depth sensors,” in Proceedings of the Vision, Modeling and Visualization, pp. 317–324, 2011.
  11. K. Khoshelham, “Accuracy analysis of kinect depth data,” in Proceedings of the ISPRS Workshop on Laser Scanning, pp. 1437–1454, 2011.
  12. S. Matyunin, D. Vatolin, Y. Berdnikov, and M. Smirnov, “Temporal filtering for depth maps generated by kinect depth camera,” in Proceedings of the 5th 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON '11), pp. 1–4, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. S.-M. Kim, Y.-C. Lee, and S.-C. Lee, “Vision based automatic inspection system for nuts welded on the support hinge,” in Proceedings of the SICE-ICASE International Joint Conference, pp. 1508–1512, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Agarwal, A. Awan, and D. Roth, “Learning to detect objects in images via a sparse, part-based representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1475–1490, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Kiryakov, B. Popov, I. Terziev, D. Manov, and D. Ognyanoff, “Semantic annotation, indexing, and retrieval,” Journal of Web Semantics, vol. 2, no. 1, pp. 49–79, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), pp. I511–I518, Kauai, Hawaii, USA, December 2001. View at Scopus
  17. Y.-F. Fung, H. Lee, and M. F. Ercan, “Image processing application in toll collection,” IAENG International Journal of Computer Science, vol. 32, pp. 473–478, 2006. View at Google Scholar
  18. M. M. Trivedi, T. Gandhi, and J. McCall, “Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 108–120, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. Open Source Computer Vision Library, http://opencv.willowgarage.com/wiki/.
  21. Real-time Computer Graphics and Physics, Mathematics, Geometry, Numerical Analysis, and Image Analysis, “Geometric tools,” http://www.geometrictools.com/LibMathematics/Approximation/Approximation.html.
  22. A. Chávez-Aragón, R. Laganière, and P. Payeur, “Vision-based detection and labelling of multiple vehicle parts,” in Proceedings of the IEEE International Conference on Intelligent Transportation Systems, pp. 1273–1278, Washington, DC, USA, 2011.
  23. M. De Berg, O. Cheong, M. Van Kreveld, and M. Overmars, “Delaunay triangulations: height interpolation,” in Computational Geometry: Algorithms and Applications, pp. 191–218, Springer, 3rd edition, 2008. View at Google Scholar
  24. P. Lindstrom, “Out-of-core simplification of large polygonal models,” in Proceedigs of the 27th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '00), pp. 259–262, July 2000. View at Scopus
  25. The Visualization Toolkit, VTK, http://www.vtk.org/.
  26. Stanford Triangle Forma, PLY, http://www.cs.virginia.edu/~gfx/Courses/2001/Advanced.spring.01/plylib/Ply.txt.
  27. P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239–256, February 1992. View at Publisher · View at Google Scholar · View at Scopus