Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 452803, 14 pages
http://dx.doi.org/10.1155/2014/452803
Research Article

Feature Based Stereo Matching Using Two-Step Expansion

1School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2National Institute of Metrology, Beijing 100029, China

Received 2 January 2014; Revised 19 June 2014; Accepted 21 July 2014; Published 18 December 2014

Academic Editor: Yi Chen

Copyright © 2014 Liqiang Wang 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. D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” International Journal of Computer Vision, vol. 47, no. 1–3, pp. 7–42, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. A. F. Bobick and S. S. Intille, “Large occlusion stereo,” International Journal of Computer Vision, vol. 33, no. 3, pp. 181–200, 1999. View at Publisher · View at Google Scholar · View at Scopus
  3. J. C. Kim, K. M. Lee, B. T. Choi, and S. U. Lee, “A dense stereo matching using two-pass dynamic programming with generalized ground control points,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1075–1082, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Sun, N. Zheng, and H. Shum, “Stereo matching using belief propagation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 787–800, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Sadeghi, P. Moallem, and S. A. Monadjemi, “Feature based dense stereo matching using dynamic programming and color,” International Journal of Computational Intelligence, vol. 4, no. 3, p. 179, 2008. View at Google Scholar
  6. L. Valgaerts, A. Bruhn, M. Mainberger, and J. Weickert, “Dense versus sparse approaches for estimating the fundamental matrix,” International Journal of Computer Vision, vol. 96, no. 2, pp. 212–234, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  7. A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Proceedings of the 10th Asian Conference on Computer Vision (ACCV '10), November 2010.
  8. B. M. Smith, L. Zhang, and H. Jin, “Stereo matching with nonparametric smoothness priors in feature space,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '09), pp. 485–492, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Tang, H. T. Tsui, and C. K. Wu, “Dense stereo matching based on propagation with a Voronoi diagram,” in Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, vol. 22, 2002.
  10. M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 993–1008, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” Image and Vision Computing, vol. 22, no. 10, pp. 761–767, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Strecha, T. Tuytelaars, and L. van Gool, “Dense matching of multiple wide-baseline views,” in Proceedings of the 9th IEEE International Conference On Computer Vision, pp. 1194–1201, October 2003. View at Scopus
  13. Q. Chen and G. Medioni, “Volumetric stereo matching method: application to image-based modeling,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), pp. 1029–1034, Fort Collins, Colo, USA, June 1999. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Gong and Y. Yang, “Fast stereo matching using reliability-based dynamic programming and consistency constraints,” in Proceedings of the 9th IEEE International Conference on Computer Vision, pp. 610–617, October 2003. View at Scopus
  15. M. Lhuillier and L. Quan, “Match propagation for image-based modeling and rendering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1140–1146, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Wu, Z. Song, J. Yao, L. Li, and Y. Gu, “Stereo matching based on support points propagation,” in Proceeding of IEEE International Conferemce on Information Science and Technology, pp. 23–25, IEEE, Hubei, China, March 2012. View at Publisher · View at Google Scholar
  17. G. Zeng, S. Paris, L. Quan, and F. Sillion, “Accurate and scalable surface representation and reconstruction from images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 141–158, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Zeng, S. Paris, L. Quan, and M. Lhuillier, “Surface reconstruction by propagating 3D stereo data in multiple 2D images,” in Proceedings of the European Conference on Computer Vision, pp. 163–174, 2004.
  19. J. Cech and R. Sara, “Efficient sampling of disparity space for fast and accurate matching,” in Proceedings of the International Workshop on Benchmarking Automated Calibration, Orientation, and Surface Reconstruction from Images, 2007.
  20. L. Wang, H. Jin, and R. Yang, “Search space reduction for MRF stereo,” in Proceedings of the European Conference on Computer Vision, 2008.
  21. L. Wang and R. Yang, “Global stereo matching leveraged by sparse ground control points,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3033–3040, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the 4th Alvey Vision Conference, pp. 147–151, 1988.
  23. 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
  24. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Geiger, M. Roser, and R. Urtasun, “Urban Scenes Dataset,” 2013, http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php.
  26. S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, “A comparison and evaluation of multi-view stereo reconstruction algorithms,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), pp. 519–526, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Weber, M. Humenberger, and W. Kubinger, “A very fast census-based stereo matching implementation on a graphics processing unit,” in Proceedings of the 12th International Conference on Computer Vision Workshops (ICCV '09), pp. 786–793, IEEE, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Bensrhair, P. Miché, and R. Debrie, “Fast and automatic stereo vision matching algorithm based on dynamic programming method,” Pattern Recognition Letters, vol. 17, no. 5, pp. 457–466, 1996. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Birchfield and C. Tomasi, “Depth discontinuities by pixel-to-pixel stereo,” International Journal of Computer Vision, vol. 35, no. 3, pp. 269–293, 1999. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Ohta and T. Kanade, “Stereo by intra- and inter- scanline search using dynamic programming,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 2, pp. 139–154, 1985. View at Google Scholar · View at Scopus
  31. O. Veksler, “Stereo correspondence by dynamic programming on a tree,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 384–390, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. O. D. Faugeras and R. Keriven, “Complete dense stereovision using level set methods,” in Proceedings of the European Conference on Computer Vision, June 1998.
  33. K. N. Kutulakos and S. M. Seitz, “Theory of shape by space carving,” International Journal of Computer Vision, vol. 38, no. 3, pp. 199–218, 2000. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Alvarez, R. Deriche, J. Sánchez, and J. Weickert, “Dense disparity map estimation respecting image discontinuities: a PDE and scale-space based approach,” Journal of Visual Communication and Image Representation, vol. 13, no. 1-2, pp. 3–21, 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Strecha, R. Fransens, and L. van Gool, “Combined depth and outlier estimation in multi-view stereo,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2394–2401, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. S. M. Seitz and C. R. Dyer, “Photorealistic scene reconstruction by voxel coloring,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1067–1073, June 1997. View at Scopus
  37. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Yedidia, W. T. Freeman, and Y. Weiss, “Understanding belief propagation and its generalizations,” in Proceedings of the International Joint Conference on Artificial Intelligence, Distinguished Papers Track, 2001.
  39. V. Kolmogorov and R. Zabih, “Multi-camera scene reconstruction via graph cuts,” in Proceedings of the European Conference on Computer Vision, pp. 82–96, 2002.
  40. C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz, “Fast cost-volume filtering for visual correspondence and beyond,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 3017–3024, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. P. Pritchett and A. Zisserman, “Wide baseline stereo matching,” in Proceedings of the 6th International Conference on Computer Vision, pp. 754–760, IEEE, January 1998. View at Scopus
  42. E. Tola, V. Lepetit, and P. Fua, “A fast local descriptor for dense matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, Anchorage, Alaska, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. T. Tuytelaars and L. V. Gool, “Wide baseline stereo matching based on local, affinely invariant regions,” in Proceedings of the British Machine Vision Conference, pp. 412–425, 2000.
  44. H. Bay, A. Ess, T. Tuytelaars, and L. van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. 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
  46. S. M. Smith and J. M. Brady, “SUSAN: a new approach to low level image processing,” International Journal of Computer Vision, vol. 23, no. 1, pp. 45–78, 1997. View at Publisher · View at Google Scholar · View at Scopus
  47. E. Tola, V. Lepetit, and P. Fua, “DAISY: an efficient dense descriptor applied to wide-baseline stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 815–830, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. R. M. Haralick and L. G. Shapiro, “Image segmentation techniques.,” Computer Vision, Graphics, & Image Processing, vol. 29, no. 1, pp. 100–132, 1985. View at Publisher · View at Google Scholar · View at Scopus
  49. G. Otto and T. Chau, “‘Region-growing’ algorithm for matching of terrain images,” Image and Vision Computing, vol. 7, no. 2, pp. 83–94, 1989. View at Publisher · View at Google Scholar · View at Scopus
  50. M. O'Neill and M. Denos, “Practical approach to the stereo matching of urban imagery,” Image and Vision Computing, vol. 10, no. 2, pp. 89–98, 1992. View at Publisher · View at Google Scholar · View at Scopus
  51. T. Kim and J. Muller, “Automated urban area building extraction from high resolution stereo imagery,” Image and Vision Computing, vol. 14, no. 2, pp. 115–130, 1996. View at Publisher · View at Google Scholar · View at Scopus
  52. M. Lhuillier and L. Quan, “A quasi-dense approach to surface reconstruction from uncalibrated images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 418–433, 2005. View at Publisher · View at Google Scholar · View at Scopus
  53. J. Kannala and S. S. Brandt, “Quasi-dense wide baseline matching using match propagation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), June 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. Z. Megyesi, G. Kós, and D. Chetverikov, “Dense 3D reconstruction from images by normal aided matching,” Machine Graphics and Vision, vol. 15, no. 1, pp. 3–28, 2006. View at Google Scholar · View at Scopus
  55. J. Čech, J. Sanchez-Riera, and R. Horaud, “Scene flow estimation by growing correspondence seeds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 3129–3136, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. C. Vogel, S. Roth, and K. Schindler, “Piecewise rigid scene flow,” in Proceedings of the International Conference on Computer Vision, 2013.
  57. K. Yamaguchi, D. McAllester, and R. Urtasun, “Robust monocular epipolar flow estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013.
  58. 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 Zentralblatt MATH · View at MathSciNet · View at Scopus
  59. K. Mikolajczyk, T. Tuytelaars, C. Schmid et al., “A comparison of affine region detectors,” International Journal of Computer Vision, vol. 65, no. 1-2, pp. 43–72, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. J. S. Beis and D. G. Lowe, “Shape indexing using approximate nearest-neighbour search in high-dimensional spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1000–1006, June 1997. View at Scopus
  61. “Edge Detection and Image Segmentation (EDISON) System,” 2014, http://coehttp://www.rutgers.edu/riul/research/code/EDISON/doc/overview.html.
  62. J. Cech and R. Sara, Cech GCS Dataset, 2013, http://cmp.felk.cvut.cz/~cechj/GCS/.
  63. D. Scharstein and R. Szeliski, “Middlebury Stereo Matching Benchmark,” 2013, http://vision.middlebury.edu/stereo/.
  64. Q. Yang, L. Wang, and N. Ahuja, “A constant-space belief propagation algorithm for stereo matching,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 1458–1465, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus