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The Scientific World Journal
Volume 2014 (2014), Article ID 513417, 12 pages
http://dx.doi.org/10.1155/2014/513417
Research Article

Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

1Computer Engineering Department, Government Engineering College, Gandhinagar, Gujarat 382028, India
2Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India

Received 19 April 2014; Revised 27 August 2014; Accepted 7 September 2014; Published 20 October 2014

Academic Editor: Andrea Torsello

Copyright © 2014 Viral H. Borisagar and Mukesh A. Zaveri. 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. M. Okutomi and T. Kanade, “Multiple-baseline stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 4, pp. 353–363, 1993. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Fusiello, V. Roberto, and E. Trucco, “Efficient stereo with multiple windowing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 858–863, San Juan, Puerto Rico, June 1997. View at Scopus
  4. D. Geiger, B. Ladendorf, and A. L. Yuille, “Occlusions and binocular stereo,” in Proceedings of the 2nd European Conference on Computer Vision, pp. 425–433, 1992.
  5. O. Veksler, “Fast variable window for stereo correspondence using integral images,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-556–I-561, June 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Ma and W. Liu, “Progressive matching based on segmentation for 3D reconstruction,” in Proceedings of the 5th International Conference on Computer and Information Technology (CIT '05), pp. 575–579, Shanghai, China, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Klančar, M. Kristan, and R. Karba, “Wide-angle camera distortions and non-uniform illumination in mobile robot tracking,” Robotics and Autonomous Systems, vol. 46, no. 2, pp. 125–133, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Cai and R. Walker, “Height estimation from monocular image sequences using dynamic programming with explicit occlusions,” IET Computer Vision, vol. 4, no. 3, pp. 149–161, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2475–2489, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. H. Sadeghi, P. Moallem, and S. A. Monadjemi, “Feature based dense stereo matching using dynamic programming and color,” International Journal of Information and Mathematical Sciences, vol. 4, no. 3, pp. 179–186, 2008. View at Google Scholar
  11. 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
  12. O. Veksler, Efficient graph-based energy minimization methods in computer vision [Ph.D. thesis], Cornell University, 1999.
  13. L. Hong and G. Chen, “Segment-based stereo matching using graph cuts,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. I-74–I-81, usa, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient belief propagation for early vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), pp. I261–I268, July 2004. View at Scopus
  15. J. Sun, N.-N. Zheng, and H.-Y. 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
  16. J. Sun, Y. Li, S. B. Kang, and H.-Y. Shum, “Symmetric stereo matching for occlusion handling,” in Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 399–406, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Birchfield and C. Tomasi, “Depth discontinuities by pixel-to-pixel stereo,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 1073–1080, Bombay, India, January 1998. View at Scopus
  18. 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
  19. S. Forstmann, Y. Kanou, J. Ohya, S. Thuering, and A. Schmitt, “Real-time stereo by using dynamic programming,” in Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, p. 29, June 2004.
  20. A. L. Yuille and T. Poggio, A Generalized Ordering Constraint for Stereo Correspondence, A.I. Memo 777, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Mass, USA, 1984.
  21. [Online], http://vision.middlebury.edu/stereo/.
  22. L. Nalpantidis, J. Kalomiros, and A. Gasteratos, “Robust 3D vision for robots using dynamic programming,” in Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '11), pp. 89–93, Penang, Malaysia, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Transactions on Medical Imaging, vol. 7, no. 4, pp. 304–312, 1988. View at Publisher · View at Google Scholar · View at Scopus
  24. H. H. Baker and T. O. Binford, “Depth from edge and intensity based stereo,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI '81), pp. 631–636, 1981. View at Scopus
  25. G. Buchsbaum, “A spatial processor model for object colour perception,” Journal of the Franklin Institute, vol. 310, no. 1, pp. 1–26, 1980. View at Publisher · View at Google Scholar · View at Scopus
  26. G. D. Finlayson, B. Schiele, and J. L. Crowley, “Comprehensive colour image normalization,” in Proceedings of the 5th European Conference on Computer Vision (ECCV '98), vol. 1, pp. 475–490, 1998.
  27. J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2207–2214, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. H. Hirschmüller and D. Scharstein, “Evaluation of cost functions for stereo matching,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR '07), pp. 1–8, Minneapolis, Minn, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. S. Heo, K. M. Lee, and S. U. Lee, “Robust Stereo matching using adaptive normalized cross-correlation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 807–822, 2011. 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. P. N. Belhumeur, “A Bayesian approach to binocular steropsis,” International Journal of Computer Vision, vol. 19, no. 3, pp. 237–260, 1996. View at Publisher · View at Google Scholar · View at Scopus
  32. I. J. Cox, S. L. Hingorani, S. B. Rao, and B. M. Maggs, “A maximum likelihood stereo algorithm,” Computer Vision and Image Understanding, vol. 63, no. 3, pp. 542–567, 1996. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Gong and Y. H. Yang, “Real-time stereo matching using orthogonal reliability-based dynamic programming,” IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 879–884, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. 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 (CVPR '05), vol. 2, pp. 1075–1082, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. L. Wang, M. Liao, M. Gong, R. Yang, and D. Nister, “High-quality real-time stereo using adaptive cost aggregation and dynamic programming,” in Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission, pp. 798–805, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Klaus, M. Sormann, and K. Karner, “Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure,” in Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), vol. 3, pp. 15–18, Hong Kong, China, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. Y.-P. Hung, C.-S. Chen, K.-C. Hung, Y.-S. Chen, and C.-S. Fuh, “Multipass hierarchical stereo matching for generation of digital terrain models from aerial images,” Machine Vision and Applications, vol. 10, no. 5-6, pp. 280–291, 1998. View at Publisher · View at Google Scholar · View at Scopus
  38. L. Zhang, “Fast stereo matching algorithm for intermediate view reconstruction of stereoscopic television images,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 10, pp. 1259–1270, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. K. S. Kumar and U. B. Desai, “New algorithms for 3D surface description from binocular stereo using integration,” Journal of the Franklin Institute, vol. 331, no. 5, pp. 531–554, 1994. View at Publisher · View at Google Scholar · View at Scopus
  40. F. Ackermann and M. Hahn, “Image pyramids for digital photogrammetry,” in Digital Photogrammetric Systems, pp. 43–58, Wichmann, Karlsruhe, Germany, 1991. View at Google Scholar
  41. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Publisher · View at Google Scholar · View at Scopus
  42. http://en.wikipedia.org/wiki/Homomorphic_filtering.
  43. V. Madisetti and D. B. Williams, Eds., Digital Signal Processing Handbook, CRC Press, Boca Raton, Fla, USA, 1999, CD-ROM.
  44. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2008.
  45. B. Horn, Robot Vision, The MIT Press, 1986.
  46. S. A. M. Saleh and H. Ibrahim, “Mathematical equations for homomorphic filtering in frequency domain: a literature survey,” in Proceedings of the International Conference on Information and Knowledge Management, pp. 74–77, Singapore, 2012.
  47. F. Singels, Real-time stereo reconstruction using hierarchical dynamic programming and LULU filtering [M.S. thesis], Stellenbosch University, 2010.
  48. J. Cai, “Integration of optical flow and dynamic programming for stereo matching,” IET Image Processing, vol. 6, no. 3, pp. 205–212, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  49. P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Transactions on Image Processing, vol. 7, no. 1, pp. 27–41, 1998. View at Publisher · View at Google Scholar · View at Scopus
  50. A. Koschan, V. Rodehorst, and K. Spiller, “Color stereo vision using hierarchical block matching and active color illumination,” in Proceedings of the 13th International Conference on Pattern Recognition, pp. 835–839, Vienna, Austria, August 1996. View at Publisher · View at Google Scholar · View at Scopus
  51. G. van Meerbergen, M. Vergauwen, M. Pollefeys, and L. van Gool, “A hierarchical symmetric stereo algorithm using dynamic programming,” International Journal of Computer Vision, vol. 47, no. 1–3, pp. 275–285, 2002. View at Publisher · View at Google Scholar · View at Scopus
  52. A. Banno and K. Ikeuchi, “Disparity map refinement and 3D surface smoothing via directed anisotropic diffusion,” in Proceedings of the 12th IEEE International Conference on Computer Vision Workshops (ICCV Workshops '09), pp. 1870–1877, Kyoto, Japan, October 2009. View at Publisher · View at Google Scholar · View at Scopus