Ralf Haeusler
ABSTRACT
Confidence measures for stereo vision are already popular for some time. Yet, comprehensive results on their performance evaluation are rare. There is still not yet any agreement on answering the question 'what is a good confidence measure'. Very little work has been done for improving discriminativity by exploiting information from combinations of different confidence measures. We present a method to determine an upper bound for performance increase possible by combining given confidence measures. We also provide a solution for a fusion of measures resulting in improved confidence accuracy on popular stereo benchmark data.
- J. Banks and P. I. Corke. Quantitative evaluation of matching methods and validity measures for stereo vision. I. J. Robotic Res., 20(7): 512--532, 2001.Google Scholar
Cross Ref
- G. Egnal. Mutual information as a stereo correspondence measure. Computer and Information Science, University of Pennsylvania, Philadelphia, USA, Tech. Rep. MS-CIS-00-20, 2000.Google Scholar
- S. K. Gehrig and T. Scharwächter. A real-time multi-cue framework for determining optical flow confidence. In ICCV Workshops, pages 1978--1985, 2011.Google ScholarCross Ref
- R. Haeusler. Evaluation of Confidence Measures on SGM Stereo. In IVCNZ, 2011.Google Scholar
- R. Haeusler and R. Klette. Analysis of KITTI Data for Stereo Analysis with Stereo Confidence Measures. In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, 2012.Google Scholar
- H. Hirschmüller. Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell., 30(2): 328--341, 2008. Google ScholarDigital Library
- H. Hirschmüller and D. Scharstein. Evaluation of cost functions for stereo matching. In CVPR, 2007.Google ScholarCross Ref
- X. Hu and P. Mordohai. A quantitative evaluation of confidence measures for stereo vision. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2012. Google ScholarDigital Library
- 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 ICCV, pages 1--8, 2007.Google ScholarCross Ref
- S. Morales and R. Klette. A third eye for performance evaluation in stereo sequence analysis. In CAIP, pages 1078--1086, 2009. Google ScholarDigital Library
- P. Mordohai. The self-aware matching measure for stereo. In ICCV, pages 1841--1848, 2009.Google ScholarCross Ref
- M. Okutomi, Y. Katayama, and S. Oka. A simple stereo algorithm to recover precise object boundaries and smooth surfaces. International Journal of Computer Vision, 47(1--3): 261--273, 2002. Google ScholarDigital Library
- D. Scharstein and C. Pal. Learning conditional random fields for stereo. In CVPR, 2007.Google ScholarCross Ref
- D. Scharstein and R. Szeliski. Stereo matching with nonlinear diffusion. International Journal of Computer Vision, 28(2): 155--174, 1998. Google ScholarDigital Library
- D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1--3): 7--42, 2002. Google ScholarDigital Library
- D. Scharstein and R. Szeliski. High-accuracy stereo depth maps using structured light. In CVPR (1), pages 195--202, 2003. Google ScholarDigital Library
- J. Shi and C. Tomasi. Good features to track. In CVPR, IEEE, pages 593--600, 1994.Google Scholar
- R. Szeliski. Prediction error as a quality metric for motion and stereo. In ICCV, pages 781--788, 1999. Google ScholarDigital Library
- A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke, and D. Cremers. Stereoscopic scene flow computation for 3d motion understanding. International Journal of Computer Vision, 95(1): 29--51, 2011. Google ScholarDigital Library
- K.-J. Yoon and I.-S. Kweon. Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell., 28(4): 650--656, 2006. Google ScholarDigital Library
Recommendations
Ensemble Learning for Confidence Measures in Stereo Vision
CVPR '13: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern RecognitionWith the aim to improve accuracy of stereo confidence measures, we apply the random decision forest framework to a large set of diverse stereo confidence measures. Learning and testing sets were drawn from the recently introduced KITTI dataset, which ...
A Robust Stereo Vision with Confidence Measure Based on Tree Agreement
Image and Video TechnologyAbstractWe present an improved non-learning-based confidence measure based on tree agreement for the stereo matching problem. To use confidence information for accurate matching, we propose an improved method to assemble a cost aggregation table with ...
A New Selective Confidence Measure---Based Approach for Stereo Matching
KES '09: Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part IAchieving an accurate disparity map in a reasonable processing time is a real challenge in the stereovision field. For this purpose, we propose in this paper an original approach which aims to accelerate matching time while keeping a very good matching ...
Comments