DOI QR코드

DOI QR Code

GPU-Based Optimization of Self-Organizing Map Feature Matching for Real-Time Stereo Vision

  • Sharma, Kajal (Department of Computer Engineering, Chosun University) ;
  • Saifullah, Saifullah (Department of Computer Engineering, Chosun University) ;
  • Moon, Inkyu (Department of Computer Engineering, Chosun University)
  • Received : 2013.12.09
  • Accepted : 2014.04.09
  • Published : 2014.06.30

Abstract

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

Keywords

References

  1. 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. https://doi.org/10.1109/TPAMI.2003.1217603
  2. T. Pribanic, N. Obradovic, and J. Salvi, "Stereo computation combining structured light and passive stereo matching," Optics Communications, vol. 285, no. 6, pp. 1017-1022, 2012. https://doi.org/10.1016/j.optcom.2011.10.045
  3. C. H. Lee, Y. C. Lim, S. Kwon, and J. H. Lee, "Stereo vision-based vehicle detection using a road feature and disparity histogram," Optical Engineering, vol. 50, no. 2, pp. 027004-027004, 2011. https://doi.org/10.1117/1.3535590
  4. S. Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, 2002. https://doi.org/10.1109/34.993558
  5. J. Shi and C. Tomasi, "Good features to track," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 593-600, 1994.
  6. K. Mikolajczyk and C. Schmid, "Scale & affine invariant interest point detectors," International Journal of Computer Vision, vol. 60, no. 1, pp. 63-86, 2004. https://doi.org/10.1023/B:VISI.0000027790.02288.f2
  7. C. Schmid and R. Mohr, "Local grayvalue invariants for image retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530-534, 1997. https://doi.org/10.1109/34.589215
  8. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  9. K. Sharma, S. G. Kim, and M. P. Singh, "An improved feature matching technique for stereo vision applications with the use of self-organizing map," International Journal of Precision Engineering and Manufacturing, vol. 13, no. 8, pp. 1359-1368, 2012. https://doi.org/10.1007/s12541-012-0179-z
  10. S. N. Sinha, J. M. Frahm, M. Pollefeys, and Y. Genc, "Feature tracking and matching in video using programmable graphics hardware," Machine Vision and Applications, vol. 22, no. 1, pp. 207-217, 2011. https://doi.org/10.1007/s00138-007-0105-z
  11. K. A. Bjorke, "Image processing on parallel GPU pixel units," Proceedings of SPIE, vol. 6065, pp. 606515, 2006.
  12. J. Fung and S. Mann, "OpenVIDIA: parallel GPU computer vision," in Proceedings of the 13th Annual ACM international conference on Multimedia, Singapore, pp. 849-852, 2005.
  13. R. Yang and M, Pollefeys, "Multi-resolution real-time stereo on commodity graphics hardware," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, pp. 211-217, 2003.
  14. C. Zach, K. Karner, and H. Bischof, "Hierarchical disparity estimation with programmable graphics hardware," in Proceedings of the 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen-Bory, Czech Republic, pp. 275-282, 2004.
  15. M. Bramberger, J. Brunner, B. Rinner, and H. Schwabach, "Realtime video analysis on an embedded smart camera for traffic surveillance," in Proceedings of the 10th IEEE Real-Time and Embedded Technology and Applications Symposium, Toronto, Canada, pp. 174-181, 2004.
  16. S. Klupsch, M. Ernst, S. A. Huss, M. Rumpf, and R. Strzodka, "Real time image processing based on reconfigurable hardware acceleration," in Proceedings of the IEEE Workshop Heterogeneous Reconfigurable Systems on Chip (SoC), Hamburg, Germany, p. 1-7, 2002.
  17. M. T. Jones and P. E. Plassmann, "Scalable iterative solution of sparse linear systems," Parallel Computing, vol. 20, no. 5, pp. 753-773, 1994. https://doi.org/10.1016/0167-8191(94)90004-3
  18. Y. Saad, "ILUM: a multi-elimination ILU preconditioner for general sparse matrices," SIAM Journal on Scientific Computing, vol. 17, no. 4, pp. 830-847, 1996. https://doi.org/10.1137/0917054
  19. T. Kohonen, "The self-organizing map," Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990. https://doi.org/10.1109/5.58325
  20. G. Toulminet, M. Bertozzi, S. Mousset, A. Bensrhair, and A. Broggi, "Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis," IEEE Transactions on Image Processing, vol. 15, no. 8, pp. 2364-2375, 2006. https://doi.org/10.1109/TIP.2006.875174
  21. D. Kirk and W. Hwu, Programming Massively Parallel Processors: A Hands-On Approach. Burlington, MA: Morgan Kaufmann Publisher, 2010.

Cited by

  1. Studies of vision monitoring system using a background separation algorithm during radiotherapy vol.20, pp.2, 2016, https://doi.org/10.6109/jkiice.2016.20.2.359