Analysis of KITTI data for stereo analysis with stereo confidence measures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7584 LNCS, Issue PART 2, 2012, Pages 158-167

  Haeusler, Ralf ^a   Klette, Reinhard ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; STEREO IMAGE PROCESSING;

CONFIDENCE MEASURE; CONSISTENCY CHECKS; COST-FUNCTION; DISPARITY MAP; GROUND TRUTH; LOCAL INTERPOLATION; PARTIAL GROUND; SPARSIFICATION; STEREO ANALYSIS; STEREO IMAGERY;

COST FUNCTIONS;

EID: 84867739761     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33868-7_16     Document Type: Conference Paper

References (19)

1. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7-42 (2002)
2. The University of Auckland: .enpeda.. image sequence analysis test site (EISATS), http://www.mi.auckland.ac.nz/EISATS
3. Heidelberg Collaboratory for Image Processing: Robust Vision Challenge, http://hci.iwr.uni-heidelberg.de/Static/challenge2012/
4. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Computer Vision and Pattern Recognition (CVPR), Providence, USA (2012)
5. Banks, J., Corke, P.I.: Quantitative evaluation of matching methods and validity measures for stereo vision. I. J. Robotic Res. 20, 512-532 (2001)
6. Hu, X., Mordohai, P.: Evaluation of stereo confidence indoors and outdoors. In: [19], pp. 1466-1473
7. Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U., Cremers, D.: Stereoscopic scene flow computation for 3D motion understanding. International Journal of Computer Vision 95, 29-51 (2011)
8. Shimizu, M., Okutomi, M.: Precise sub-pixel estimation on area-based matching. In: ICCV, pp. 90-97 (2001)
9. Merrell, P., Akbarzadeh, A., Wang, L., Mordohai, P., Frahm, J.-M., Yang, R., Nistér, D., Pollefeys, M.: Real-time visibility-based fusion of depth maps. In: ICCV, pp. 1-8 (2007)
10. Gehrig, S.K., Scharwächter, T.: A real-time multi-cue framework for determining optical flow confidence. In: ICCV Workshops, pp. 1978-1985 (2011)
11. Aodha, O.M., Brostow, G.J., Pollefeys, M.: Segmenting video into classes of algorithm-suitability. In: [19], pp. 1054-1061
12. Humayun, A., Aodha, O.M., Brostow, G.J.: Learning to find occlusion regions. In: CVPR, pp. 2161-2168. IEEE (2011)
13. Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30, 328-341 (2008)
14. Egnal, G.: Mutual information as a stereo correspondence measure. Computer and Information Science, University of Pennsylvania, Philadelphia, USA, Tech. Rep. MS-CIS-00-20 (2000)
15. Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: CVPR (2007)
16. Scharstein, D., Szeliski, R.: Stereo matching with nonlinear diffusion. International Journal of Computer Vision 28, 155-174 (1998)
17. Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593-600. IEEE (1994)
18. Okutomi, M., Katayama, Y., Oka, S.: A simple stereo algorithm to recover precise object boundaries and smooth surfaces. International Journal of Computer Vision 47, 261-273 (2002)
19. The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, June 13-18. IEEE (2010)