Matching of Line Segment for Stereo Computation

O. Martorell, A. Buades, B. Coll

2017

Abstract

A stereo algorithm based on the matching of line segments between two images is proposed. We extract several characteristics of the segments which permit its matching across the two images. A depth ordering computed from the line segments of the reference image allows us to attribute the match disparity to the correct pixels. This depth sketch is computed by joining close line segments and identifying T-junctions and convexity points. The disparity computed for segments is then extrapolated to the rest of the image by means of a diffusion process. The performance of the proposed algorithm is illustrated by applying the procedure to synthetic stereo pairs.

References

  1. Brown, M. Z., Burschka, D., and Hager, G. D. (2003). Advances in computational stereo. IEEE Trans. Pattern Anal. Mach. Intell., 25(8):993-1008.
  2. Buades, A. and Facciolo, G. (2015). Reliable multiscale and multiwindow stereo matching. SIAM Journal on Imaging Sciences, 8(2):888-915.
  3. Burns, J., Hanson, H. R., and Riseman, E. M. (1986). Extracting straight lines. EEE Transactions on PAMI, 8(4):425-455.
  4. Calderero, F. and Caselles, V. (2013). Recovering relative depth from low-level features without explicit tjunction detection and interpretation. Int. J. Comput. Vision, 104(1):38-68.
  5. Desolneux, A., Moisan, L., and Morel, J. (2000). Meaningful alignments. International Journal of Computer Vision, 40(1):7-23.
  6. Dimiccoli, M., Morel, J.-M., and Salembier, P. (2008). Monocular depth by nonlinear diffusion. In Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, ICVGIP 7808, pages 95-102, Washington, DC, USA. IEEE Computer Society.
  7. Grompone von Gioi, R., Jakubowicz, J., Morel, J., and G., R. (2010). Lsd: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4):722-732.
  8. Grompone von Gioi, R., Jakubowicz, J., Morel, J.-M., and Randall, G. (2012). LSD: a Line Segment Detector. Image Processing On Line, 2:35-55.
  9. Hirschmüller, H., Innocent, P. R., and Garibaldi, J. (2002). Real-time correlation-based stereo vision with reduced border errors. International Journal of Computer Vision, 47(1-3):229-246.
  10. Kolmogorov, V. and Zabih, R. (2001). Computing visual correspondence with occlusions using graph cuts. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 2, pages 508-515. IEEE.
  11. Ma, S. D., Si, S. H., and Chen, Z. Y. (1992). Quadric curve based stereo. In ICPR 92, pages 1-4. IEEE.
  12. McCane, B. J. and de Vel, O. (1994). A stereo matching algorithm using curve segments and cluster analysis. Technical report, Citeseer.
  13. Nasrabadi, N. M. (1992). A stereo vision technique using curve-segments and relaxation matching. IEEE Trans. Pattern Anal. Mach. Intell., 14(5):566-572.
  14. Palou, G. and Salembier, P. (2013). Monocular depth ordering using t-junctions and convexity occlusion cues. IEEE Transactions on Image Processing, 22(5):1926- 1939.
  15. Patricio, M. P., Cabestaing, F., Colot, O., and Bonnet, P. (2004). A similarity-based adaptive neighborhood method for correlation-based stereo matching. In Image Processing, 2004. ICIP'04. 2004 International Conference on, volume 2, pages 1341-1344. IEEE.
  16. Scharstein, D. and Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1-3):7-42.
  17. Sun, J., Zheng, N.-N., and Shum, H.-Y. (2003). Stereo matching using belief propagation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(7):787-800.
  18. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Rother, C., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C. (2008). A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6):1068- 80.
  19. Tombari, F., Mattocia, S., Stefano, L., and Addimanda, E. (2008). Classification and evaluation of cost aggregation methods for stereo correspondence. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  20. Wang, L., Liao, M., Gong, M., Yang, R., and Nister, D. (2006). High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In 3D Data Processing, Visualization, and Transmission, Third International Symposium on, pages 798-805. IEEE.
  21. Yoon, K.-J. and Kweon, I. S. (2006). Adaptive supportweight approach for correspondence search. IEEE Transactions on Pattern Analysis & Machine Intelligence, (4):650-656.
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Paper Citation


in Harvard Style

Martorell O., Buades A. and Coll B. (2017). Matching of Line Segment for Stereo Computation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 410-417. DOI: 10.5220/0006121004100417


in Bibtex Style

@conference{visapp17,
author={O. Martorell and A. Buades and B. Coll},
title={Matching of Line Segment for Stereo Computation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={410-417},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006121004100417},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Matching of Line Segment for Stereo Computation
SN - 978-989-758-227-1
AU - Martorell O.
AU - Buades A.
AU - Coll B.
PY - 2017
SP - 410
EP - 417
DO - 10.5220/0006121004100417