The edge-aware bilateral filter has been demonstrated to be effective for preserving depth edges, and disparity maps obtained from Fast Bilateral Stereo (FBS) have enhanced the efficiency of algorithm and the robustness to noise. However, they also lead to a non-perfect localisation of discontinuities. To overcome this issue, a new bilateral filtering based cost aggregation utilising colour statistical classification and similarity measurement within annular blocks is proposed in this study. We have adopted the similarity of histograms evaluated by Earth Mover Distance (EMD) to obtain the raw matching cost in the raised annular block, since histograms are very effective and efficient in capturing the distribution characteristics of visual features. For the weights aggregation, the spatial weight is assumed to be a constant. The colour weight is calculated by using a cluster-mean-value strategy, which is implemented by the local colour histogram. It improves the accuracy in the discontinuous areas. Computation redundancy is reduced by disparity candidate selection using the local minimal relevancy in the corresponding annular blocks. We use the efficiency and accuracy to demonstrate the performance of our proposed method. Experimental results have shown that the proposed method reduces the mismatch at depth discontinuous and the computation complexity significantly.

References

1. 1)
  - 17. Sun, X., Mei, X., Jiao, S., et al: ‘Real-time local stereo via edge-aware disparity propagation’, Pattern Recognit. Lett., 2014, 49, pp. 201–206 (doi: 10.1016/j.patrec.2014.07.010).
2. 2)
  - 19. Yoon, K.J., Kweon, I.S.: ‘Adaptive support-weight approach for correspondence search’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 2006, 28, (4), pp. 650–656 (doi: 10.1109/TPAMI.2006.70).
3. 3)
  - 29. Facciolo, G., Limare, N., Meinhardt-Llopis, E.: ‘Integral images for block matching’, Image Process. On Line, 2014, 4, pp. 344–369 (doi: 10.5201/ipol.2014.57).
4. 4)
  - 1. Brown, M.Z., Burschka, D., Hager, G.D.: ‘Advances in computational stereo’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (8), pp. 993–1008 (doi: 10.1109/TPAMI.2003.1217603).
5. 5)
  - 23. Scharstein, D., Szeliski, R.: ‘A taxonomy and evaluation of dense two-frame stereo correspondence algorithms’, Int. J. Comput. Vis., 2002, 47, (1–3), pp. 7–42 (doi: 10.1023/A:1014573219977).
6. 6)
  - 11. Yang, Q., Wang, L., Yang, R., et al: ‘Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (3), pp. 492–504 (doi: 10.1109/TPAMI.2008.99).
7. 7)
  - 21. Paris, S., Durand, F.: ‘A fast approximation of the bilateral filter using a signal processing approach’, Int. J. Comput. Vis., 2009, 81, (1), pp. 24–52 (doi: 10.1007/s11263-007-0110-8).
8. 8)
  - 16. Cigla, C., Alatan, A.: ‘Information permeability for stereo matching’, Signal Process., Image Commun., 2013, 28, (9), pp. 1072–1088 (doi: 10.1016/j.image.2013.04.001).
9. 9)
  - 6. Hirschmuller, H., Innocent, P., Garibaldi, J.: ‘Real-time correlation-based stereo vision with reduced border errors’, Int. J. Comput. Vis., 2002, 47, pp. 229–246 (doi: 10.1023/A:1014554110407).
10. 10)
  - 3. Leonardo, D., Arantxa, V., Rafael, C.: ‘Stereo matching using gradient similarity and locally adaptive support-weight’, Pattern Recognit. Lett., 2011, 32, (13), pp. 1643–1651 (doi: 10.1016/j.patrec.2011.06.027).
11. 11)
  - 8. Nicolas, P., Vicent, C.: ‘Multi-label depth estimation for graph cuts stereo problems’, J. Math. Imaging Vis., 2010, 38, (1), pp. 70–82 (doi: 10.1007/s10851-010-0212-8).
12. 12)
  - 15. De Maeztu, L., Villanueva, A., Cabeza, R.: ‘Stereo matching using gradient similarity and locally adaptive support-weight’, Pattern Recognit. Lett., 2011, 32, (13), pp. 1643–1651.
13. 13)
  - 31. Gerrits, M., Bekaert, P.: ‘Local stereo matching with segmentation-based outlier rejection’. Third Canadian Conf. on Computer and Robot Vision (CRV), Canada, June 2006, pp. 66–73.
14. 14)
  - 2. Scharstein, D., Szeliski, R.: ‘A taxonomy and evaluation of dense two-frame stereo correspondence algorithms’, Int. Int. J. Comput. Vis., 2002, 47, (1/2/3), pp. 7–42.
15. 15)
  - 26. Paris, S., Kornprobst, P., Tumblin, J., et al: ‘Bilateral filtering: theory and applications’ (Foundations and Trends in Computer Graphics and Vision, Now Publishers Inc. Press, Boston, 2009).
16. 16)
  - 17. Sun, X., Mei, X., Jiao, S., et al: ‘Real-time local stereo via edge-aware disparity propagation’, Pattern Recognit. Lett., 2014, 49, pp. 201–206.
17. 17)
  - 21. Paris, S., Durand, F.: ‘A fast approximation of the bilateral filter using a signal processing approach’, Int. J. Comput. Vis., 2009, 81, (1), pp. 24–52.
18. 18)
  - 23. Yang, Q.: ‘Recursive bilateral filtering’. European Conf. on Computer Vision (ECCV), Firenze, Italy, October 2012, pp. 399–413.
19. 19)
  - 19. Yoon, K.J., Kweon, I.S.: ‘Adaptive support-weight approach for correspondence search’, IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 2006, 28, (4), pp. 650–656.
20. 20)
  - 29. Facciolo, G., Limare, N., Meinhardt-Llopis, E.: ‘Integral images for block matching’, Image Process. On Line, 2014, 4, pp. 344–369.
21. 21)
  - 14. Yang, Q.: ‘A non-local cost aggregation method for stereo matching’. CVPR, Providence, RI USA, June 2012, pp. 1402–1409.
22. 22)
  - 22. Porikli, F.M.: ‘Constant time O(1) bilateral filtering’. CVPR, Anchorage, Alaska, June 2008, pp. 3895–3902.
23. 23)
  - 20. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. ICCV, Bombay, India, January 1998, pp. 839–846.
24. 24)
  - 1. Brown, M.Z., Burschka, D., Hager, G.D.: ‘Advances in computational stereo’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (8), pp. 993–1008.
25. 25)
  - 10. Bleyer, M., Rhemann, C., Rother, C.: ‘Patch Match stereo – stereo matching with slanted support windows’. Proc. of British Machine Vision Conf. (BMVC), University of Dundee, September 2011, pp. 1–11.
26. 26)
  - 9. Wang, L., Yang, R.: ‘Global stereo matching leveraged by sparse ground control points’. Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, June 2011, pp. 3033–3040.
27. 27)
  - 6. Hirschmuller, H., Innocent, P., Garibaldi, J.: ‘Real-time correlation-based stereo vision with reduced border errors’, Int. J. Comput. Vis., 2002, 47, pp. 229–246.
28. 28)
  - 8. Nicolas, P., Vicent, C.: ‘Multi-label depth estimation for graph cuts stereo problems’, J. Math. Imaging Vis., 2010, 38, (1), pp. 70–82.
29. 29)
  - 25. ‘International Journal of Computer Vision’ available at http://vision.middlebury.edu/stereo/, accessed April 2002.
30. 30)
  - 24. Stefano, M., Simone, G., Andrea, G.: ‘Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering’. Ninth Asian Conf. on Computer Vision (ACCV), Xi'an, China, September 2009, pp. 371–380.
31. 31)
  - 11. Yang, Q., Wang, L., Yang, R., et al: ‘Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (3), pp. 492–504.
32. 32)
  - 18. Veksler, O.: ‘Stereo matching by compact window via minimum ratio cycle’. IEEE Int. Conf. Computer Vision (ICCV), British Columbia, Canada, July 2001, pp. 540–547.
33. 33)
  - 5. Pham, C., Jeon, J.: ‘Domain transformation-based efficient cost aggregation for local stereo matching’, IEEE TCSVT, 2013, 23, (7), pp. 1119–1130.
34. 34)
  - 4. Lu, J., Shi, J., Min, D., et al: ‘Cross-based local multipoint filtering’. CVPR, Providence, Rhode Island, June 2012, pp. 430–437.
35. 35)
  - 30. Tombari, F., Mattoccia, S., Di Stefano, L., et al: ‘Classification and evaluation of cost aggregation methods for stereo correspondence’. CVPR, Anchorage, Alaska, June 2008, pp. 1–8.
36. 36)
  - 3. Leonardo, D., Arantxa, V., Rafael, C.: ‘Stereo matching using gradient similarity and locally adaptive support-weight’, Pattern Recognit. Lett., 2011, 32, (13), pp. 1643–1651.
37. 37)
  - 7. Yang, Q., Wang, L., Ahuja, N.: ‘A constant-space belief propagation algorithm for stereo matching’. Proc. IEEE Computer Vision Pattern Recognition (CVPR), San Francisco, June 2010, pp. 1458–1465.
38. 38)
  - 13. Mei, X., Sun, X., Dong, W., et al: ‘Segment-tree based cost aggregation for stereo matching’. CVPR 2013, Portland, Oregon, June 2013, pp. 313–320.
39. 39)
  - 16. Cigla, C., Alatan, A.: ‘Information permeability for stereo matching’, Signal Process., Image Commun., 2013, 28, (9), pp. 1072–1088.
40. 40)
  - 12. Wang, L., Liao, M., Gong, M., et al: ‘High-quality real-time stereo using adaptive cost aggregation and dynamic programming’. Proc. 3DPVT, Chapel Hill, USA, June 2006, pp. 798–805.
41. 41)
  - 27. Tombari, F., Mattoccia, S., Di Stefano, L.: ‘Segmentation-based adaptive support for accurate stereo correspondence’. IEEE Pacific-Rim Symp. on Image and Video Technology (PSIVT). LNCS 2007, Santiago, Chile, December 2007, pp. 427–438.
42. 42)
  - 28. Li, P., Wang, Q., Zhang, L.: ‘A novel earth mover's distance methodology for image matching with gaussian mixture models’. Int. Conf. on Computer Vision (ICCV), Sydney, Australia, December 2013, pp. 1689–1696.

Histogram-based cost aggregation strategy with joint bilateral filtering for stereo matching

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