The self-aware matching measure for stereo

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 1841-1848

  Mordohai, Philippos ^a  

^a STEVENS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINOCULAR STEREO; GROUND TRUTH; INPUT IMAGE; MATCHING FUNCTIONS; SIMILARITY MEASURE; STATE OF THE ART; STEREO ALGORITHMS; STEREO MATCHING;

COMPUTER VISION; PIXELS;

METADATA;

EID: 77953227246     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459409     Document Type: Conference Paper

References (29)

1. A. Agarwal. and A. Blake. Dense stereo matching over the panum band. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009.
2. S. Birchfield and C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(4):401-406, 1998.
3. A. Bobick and S. Intille. Large occlusion stereo. Int. Journal of Computer Vision, 33(3):1-20, 1999.
4. D. Bradley, T. Boubekeur, and W. Heidrich. Accurate multiview reconstruction using robust binocular stereo and surface meshing. In Int. Conf. on Computer Vision and Pattern Recognition, 2008.
5. N. Campbell, G. Vogiatzis, C. Hernandez, and R. Cipolla. Using multiple hypotheses to improve depth-maps for multiview stereo. In European Conf. on Computer Vision, pages 766-779, 2008.
6. J. Cech and R. Sara. Efficient sampling of disparity space for fast and accurate matching. In BenCOS, 2007.
7. Q. Chen and G. Medioni. A volumetric stereo matching method: Application to image-based modeling. In Int. Conf. on Computer Vision and Pattern Recognition, pages I: 29-34, 1999.
8. G. Egnal, M. Mintz, and R. Wildes. A stereo confidence metric using single view imagery with comparison to five alternative approaches. Image and Vistion Computing, 22(12):943-957, 2004.
9. M. Goesele, B. Curless, and S. M. Seitz. Multi-view stereo revisited. In Int. Conf. on Computer Vision and Pattern Recognition, pages 2402-2409, 2006.
10. M. Gong, R. Yang, L. Wang, and M. Gong. A performance study on different cost aggregation approaches used in real-time stereo matching. Int. Journal of Computer Vision, 75(2):283-296, 2007.
11. M. Gong and Y. Yang. Fast unambiguous stereo matching using reliability-based dynamic programming. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(6):998-1003, 2005. (Pubitemid 40889850)
12. H. Hirschmüller and D. Scharstein. Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009.
13. J. Kostliva, J. Cech, and R. Sara. Feasibility boundary in dense and semi-dense stereo matching. In BenCOS, 2007.
14. M. Lhuillier and L. Quan. Match propagation for imagebased modeling and rendering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(8):1140-1146, 2002.
15. R. Manduchi and C. Tomasi. Distinctiveness maps for image matching. In Int. Conf. on Image Analysis and Processing, pages 26-31, 1999.
16. L. Matthies. Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation. SPIE, 1570:187-200, 1991.
17. P. Merrell, A. Akbarzadeh, L. Wang, P. Mordohai, J.-M. Frahm, R. Yang, D. Nistér, and M. Pollefeys. Real-time visibility-based fusion of depth maps. In Int. Conf. on Computer Vision, 2007.
18. H. Moravec. Rover visual obstacle avoidance. In Int. Joint Conf. on Artificial Intelligence, pages 785-790, 1981.
19. E. Salari and J. Strong. On the reliability of correlation based stereo matching. In IEEE Int. Conf. on Systems Engineering, pages 559-561, 1990.
20. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Journal of Computer Vision, 47(1-3):7-42, 2002.
21. C. Tomasi and R. Manduchi. Stereo matching as a nearestneighbor problem. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(3):333-340, 1998.
22. P. Viola and W. Wells, III. Alignment by maximization of mutual information. Int. Journal of Computer Vision, 24(2):137-154, 1997.
23. K. Yoon and I. Kweon. Adaptive support-weight approach for correspondence search. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(4):650-656, 2006.
24. K. Yoon and I. Kweon. Stereo matching with the distinctive similarity measure. In Int. Conf. on Computer Vision, 2007.
25. K. Yoon and I. Kweon. Distinctive similarity measure for stereo matching under point ambiguity. Computer Vision and Image Understanding, 112(2):173-183, 2008.
26. R. Zabih and J. Woodfill. Non-parametric local transforms for computing visual correspondence. In European Conf. on Computer Vision, pages B:151-158, 1994.
27. Y. Zhang and C. Kambhamettu. Stereo matching with segmentation-based cooperation. In European Conf. on Computer Vision, pages II: 556-571, 2002.
28. Z. Zhang and Y. Shan. A progressive scheme for stereo matching. In Lecture Notes in Computer Science, 2018, pages 68-85, 2001.
29. C. Zitnick and T. Kanade. A cooperative algorithm for stereo matching and occlusion detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(7):675-684, 2000.