Table of Contents
International Journal of Vehicular Technology
Volume 2011, Article ID 326865, 11 pages
http://dx.doi.org/10.1155/2011/326865
Research Article

Stereo Image Matching for Vehicle-Borne Mobile Mapping System Based on Digital Parallax Model

Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 WenYuan Road, Nanjing, Jiangsu 210046, China

Received 2 November 2010; Revised 18 January 2011; Accepted 12 April 2011

Academic Editor: Nandana Rajatheva

Copyright © 2011 Ka Zhang 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. H. J. Zhao and R. Shibasaki, “Reconstructing a textured CAD model of an urban environment using vehicle-borne laser range scanners and line cameras,” Machine Vision and Applications, vol. 14, no. 1, pp. 35–41, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Grinstead, A. Koschan, D. Page, A. Gribok, and M. A. Abidi, “Vehicle-borne scanning for detailed 3D terrain model generation,” in Proceedings of the SAE Commercial Vehicle Engineering Congress and Exhibition, pp. 210–218, Chicago, Ill, USA, November 2005.
  3. K. Ishikawa, J. Takiguchi, Y. Amano, and T. Hashizume, “A mobile mapping system for road data capture based on 3D road model,” in Proceedings of the IEEE International Conference on Control Applications, pp. 638–643, Munich, Germany, 2006.
  4. S. J. Yu, S. R. Sukumar, A. F. Koschan, D. L. Page, and M. A. Abidi, “3D reconstruction of road surfaces using an integrated multi-sensory approach,” Optics and Lasers in Engineering, vol. 45, no. 7, pp. 808–818, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. X. X. Geng and S. D. Zhong, “A mobile system using lidar and photogrammetry for urban spatial objects extraction,” in Proceedings of the International Conference on Information Engineering and Computer Science (ICIECS '09), pp. 1–4, Wuhan, China, December 2009. View at Publisher · View at Google Scholar
  6. J. M. Wanek and C. H. Wu, “Automated trinocular stereo imaging system for three-dimensional surface wave measurements,” Ocean Engineering, vol. 33, no. 5-6, pp. 723–747, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Zhao and N.-H. Wang, “Precise perimeter measurement for 3D object with a binocular stereo vision measurement system,” Optik—International Journal for Light and Electron Optics, vol. 121, no. 10, pp. 953–957, 2010. View at Google Scholar
  8. R. Kimmel, “3D shape reconstruction from autostereograms and stereo,” Journal of Visual Communication and Image Representation, vol. 13, no. 1-2, pp. 324–333, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Kim and K. Sohn, “3D reconstruction from stereo images for interactions between real and virtual objects,” Signal Processing: Image Communication, vol. 20, no. 1, pp. 61–75, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. K. H. Jang and S. K. Jung, “Practical modeling technique for large-scale 3D building models from ground images,” Pattern Recognition Letters, vol. 30, no. 10, pp. 861–869, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. A. R. Farooq, M. L. Smith, L. N. Smith, and S. Midha, “Dynamic photometric stereo for on line quality control of ceramic tiles,” Computers in Industry, vol. 56, no. 8-9, pp. 918–934, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. J. J. Aguilar, M. Lope, F. Torres, and A. Blesa, “Development of a stereo vision system for non-contact railway concrete sleepers measurement based in holographic optical elements,” Measurement, vol. 38, no. 2, pp. 154–165, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. S. H. Lai, “Robust image matching under partial occlusion and spatially varying illumination change,” Computer Vision and Image Understanding, vol. 78, no. 1, pp. 84–98, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. M. J. Atallah, “Faster image template matching in the sum of the absolute value of differences measure,” IEEE Transactions on Image Processing, vol. 10, no. 4, pp. 659–663, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Stow, L. Coulter, and S. Baer, “A frame centre matching approach to registration for change detection with fine spatial resolution multi-temporal imagery,” International Journal of Remote Sensing, vol. 24, no. 19, pp. 3873–3879, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Shamir, “A proposed stereo matching algorithm for noisy sets of color images,” Computers & Geosciences, vol. 33, no. 8, pp. 1052–1063, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. K. Chan and C. C. Chang, “Image matching using run-length feature,” Pattern Recognition Letters, vol. 22, no. 5, pp. 447–455, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. C. J. Zhao, W. K. Shi, and Y. Deng, “A new Hausdorff distance for image matching,” Pattern Recognition Letters, vol. 26, no. 5, pp. 581–586, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. M. E. Ansari, L. Masmoudi, and A. Bensrhair, “A new regions matching for color stereo images,” Pattern Recognition Letters, vol. 28, no. 13, pp. 1679–1687, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Zuliani, L. Bertelli, C. S. Kenney, S. Chandrasekaran, and B. S. Manjunath, “Drums, curve descriptors and affine invariant region matching,” Image and Vision Computing, vol. 26, no. 3, pp. 347–360, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Dufournaud, C. Schmid, and R. Horaud, “Image Matching with Scale Adjustment[J],” Computer Vision and Image Understanding, vol. 93, no. 2, pp. 175–194, 2004. View at Google Scholar
  22. J. Zhou, Y. Xu, and X. K. Yang, “Quaternion wavelet phase based stereo matching for uncalibrated images,” Pattern Recognition Letters, vol. 28, no. 12, pp. 1509–1522, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Maxime and Q. Long, “Roubst dense matching using local and global geometric constraints,” in Proceedings of the 16th International Conference on Pattern Recognition, vol. 1, pp. 968–972, Barcelona, Spain, 2000.
  24. J. Zhou and J. Shi, “A robust algorithm for feature point matching,” Computers & Graphics, vol. 26, no. 3, pp. 429–436, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. K. Zhang, Y. H. Sheng, Y. Q. Li, B. Han, C. Liang, and W. Sha, “Image matching for digital close-range stereo photogrammetry based on constraints of Delaunay triangulated network and epipolar-line,” in Geoinformatics 2006: Remotely Sensed Data and Information, vol. 6419 of Proceedings of SPIE, pp. 64191W_1–64191W_15, Wuhan, China, October 2006. View at Publisher · View at Google Scholar
  26. Q. Zhu, J. Zhao, H. Lin, and J. Gong, “Triangulation of well-defined points as a constraint for reliable image matching,” Photogrammetric Engineering & Remote Sensing, vol. 71, no. 9, pp. 1063–1069, 2005. View at Google Scholar · View at Scopus
  27. Q. Zhu, B. Wu, and Y. X. Tian, “Propagation strategies for stereo image matching based on the dynamic triangle constraint,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 62, no. 4, pp. 295–308, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Y. Chang, K. M. Lee, and S. U. Lee, “Stereo matching using iterative reliable disparity map expansion in the color-spatial-disparity space,” Pattern Recognition, vol. 40, no. 12, pp. 3705–3713, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. Z. L. Li and Q. Zhu, Digital Elevation Model, Wuhan University Press, Wuhan, China, 2003.
  30. Z . X. Zhang and J. Q. Zhang, Digital Photogrammetry, Wuhan University Press, Wuhan, China, 1997.
  31. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Stephen, D. G. Lowe, and J. J. Little, “Vision-based global localization and mapping for mobile robots,” IEEE Transactions on Robotics, vol. 21, no. 3, pp. 364–375, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Helmer and D. G. Lowe, “Object class recognition with many local features,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 187–194, Washington, DC, USA, 2004.
  35. J. Chen and J. Tian, “Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor,” Progress in Natural Science, vol. 19, no. 5, pp. 643–651, 2009. View at Publisher · View at Google Scholar · View at Scopus
  36. C. L. Lin and Y. M. Zhao, “Trademark retrieval algorithm based on SIFT feature,” Computer Engineering, vol. 34, no. 23, pp. 275–277, 2008. View at Google Scholar