Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free SVCV: segmentation volume combined with cost volume for stereo matching

© The Institution of Engineering and Technology
Received 28/11/2016, Accepted 23/08/2017, Published 08/09/2017

Stereo matching between binocular stereo images is fundamental to many computer vision tasks, such as three-dimensional (3D) reconstruction and robot navigation. Various structures of real 3D scenes lead stereo matching to be an old yet still challenging problem. In this study, the authors proposed a novel adaptive support weights technique which exploits the hierarchical information provided by multilevel segmentation to preserve the robustness to imaging conditions and spatial proximity in cost aggregation. Besides, a generalisable cost refinement strategy is designed to remove the matching ambiguity in large weakly textured regions. The proposed strategy utilises both the fluctuation of the filtered cost volume and the colour information to further improve the matching accuracy. Experimental results of 50 stereo images demonstrate the effectiveness and efficiency of the proposed method. Furthermore, a systematic evaluation is developed to assess the conventional steps in local stereo methods and then reliable suggestions are given to the beginners and researchers outside the stereo matching field.

Inspec keywords: stereo image processing; image matching; image segmentation

Other keywords: stereo matching; SVCV; novel adaptive support weights technique; segmentation volume; generalisable cost refinement strategy; computer vision; multilevel segmentation; binocular stereo images

Subjects: Computer vision and image processing techniques; Image recognition

References

    1. 1)
      • 11. He, K., Sun, J., Tang, X.: ‘Guided image filtering’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (6), pp. 1397–1409.
    2. 2)
      • 15. Gong, M.: ‘A performance study on different cost aggregation approaches used in real-time stereo matching’, Int. J. Comput. Vis., 2007, 2, (75), pp. 283–296.
    3. 3)
      • 16. Kong, D., Tao, H.: ‘Stereo matching via learning multiple experts behaviors’. British Machine Vision Conf., Edinburgh, United Kingdom, September 2006, pp. 1–10.
    4. 4)
      • 10. Hosni, A., Bleyer, M., Gelautz, M., et al: ‘Local stereo matching using geodesic support weights’. Int. Conf. Image Processing, Cairo, Egypt, November 2009, pp. 2093–2096.
    5. 5)
      • 26. ‘Middlebury stereo matching test bed’, 2014. Available at http://vision.middlebury.edu/stereo/eval.
    6. 6)
      • 3. Mozerov, M.G., Weijer, J.: ‘Accurate stereo matching by two-step energy minimization’, IEEE Trans. Image Process., 2015, 24, (3), pp. 1153–1163.
    7. 7)
      • 2. Szeliski, R., Zabih, R., Scharstein, D., et al: ‘A comparative study of energy minimization methods for Markov random fields with smoothness-based priors’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (6), pp. 1068–1080.
    8. 8)
      • 24. Felzenszwalb, P., Huttenlocher, D.: ‘Efficient graph-based image segmentation’, Int. J. Comput. Vis., 2004, 59, (2), pp. 167–181.
    9. 9)
      • 18. Yang, Q.: ‘A non-local cost aggregation method for stereo matching’. Int. Conf. Computer Vision and Pattern Recognition, Providence, Rhode Island, June 2012, pp. 1402–1409.
    10. 10)
      • 6. Fusiello, A., Roberto, V., Trucco, E.: ‘Efficient stereo with multiple windowing’. Proc. Int. Conf. Computer Vision and Pattern Recognition, Madison, Wisconsin, June 2003, pp. 556–561.
    11. 11)
      • 20. Zhu, H., Yin, J., Yuan, D., et al: ‘Fluctuations of disparity space image for stereo matching in untextured regions’. Int. Conf. Image Processing, Quebec, Canada, September 2015, pp. 1578–1582.
    12. 12)
      • 12. Scharstein, D., Hirschmüller, H., Kitajima, Y., et al: ‘High-resolution stereo datasets with subpixel-accurate ground truth’. German Conf. Pattern Recognition, Münster, Germany, September 2014, pp. 31–42.
    13. 13)
      • 13. Hosni, A., Bleyer, M., Gelautz, M.: ‘Secrets of adaptive support weight techniques for local stereo matching’, Comput. Vis. Image Underst., 2013, 117, (6), pp. 620–632.
    14. 14)
      • 23. Perri, S., Corsonello, P., Cocorullo, G.: ‘Adaptive census transform: a novel hardware-oriented stereovision algorithm’, Comput. Vis. Image Underst., 2013, 117, (1), pp. 29–41.
    15. 15)
      • 22. Bleyer, M., Breiteneder, C.: ‘Stereo matching-state-of-the-art and research challenges’, in Kang, S.B. (Ed.): ‘Advanced topics in computer vision’ (SpringerLondon, 2013, 1st edn.), pp. 143–179.
    16. 16)
      • 5. Boykov, Y., Veksler, O., Zabih, R.: ‘A variable window approach to early vision’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (12), pp. 1283–1294.
    17. 17)
      • 1. Scharstein, D., Szeliski, R., Zabih, R.: ‘A taxonomy and evaluation of dense two-frame stereo correspondence algorithms’, Int. J. Comput. Vis., 2002, 47, (1), pp. 7–42.
    18. 18)
      • 4. Kanade, T., Okutomi, M.: ‘A stereo matching algorithm with an adaptive window: theory and experiments’, IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, (9), pp. 920–932.
    19. 19)
      • 9. Tombari, F., Mattoccia, S., Stefano, L.D.: ‘Segmentation-based adaptive support for accurate stereo correspondence’. Pacific-Rim Symp. on Image and Video Technology, Santiago, Chile, December 2007, pp. 427–438.
    20. 20)
      • 7. Yoon, K.-J., Kweon, I.S.: ‘Adaptive support-weight approach for correspondence search’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (4), pp. 650–656.
    21. 21)
      • 8. Gerrits, M., Bekaert, P.: ‘Local stereo matching with segmentation-based outlier rejection’. Canadian Conf. Computer and Robot Vision, Quebec, Canada, June 2006, p. 66.
    22. 22)
      • 17. Hirschmüller, H.: ‘Stereo processing by semiglobal matching and mutual information’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 30, (2), pp. 328–341.
    23. 23)
      • 14. Min, D., Sohn, K.: ‘Cost aggregation and occlusion handling with WLS in stereo matching’, IEEE Trans. Image Process., 2008, 17, (8), pp. 1431–1442.
    24. 24)
      • 19. Yang, Q., Ji, P., Li, D., et al: ‘Fast stereo matching using adaptive guided filtering’, Image Vis. Comput., 2014, 32, pp. 202–211.
    25. 25)
      • 25. Rhemann, C., Hosni, A., Bleyer, M., et al: ‘Fast cost-volume filtering for visual correspondence and beyond’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (2), pp. 504–511.
    26. 26)
      • 21. Hirschmüller, H., Scharstein, D.: ‘Evaluation of stereo matching costs on images with radiometric differences’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 31, (9), pp. 1582–1599.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0446
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0446
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address