Stereo Matching with Nonlinear Diffusion

被引:0
作者
Daniel Scharstein
Richard Szeliski
机构
[1] Middlebury College,Department of Mathematics and Computer Science
[2] One Microsoft Way,Microsoft Research
来源
International Journal of Computer Vision | 1998年 / 28卷
关键词
stereo matching; variable-sized support region; nonlinear diffusion; Bayesian estimation;
D O I
暂无
中图分类号
学科分类号
摘要
One of the central problems in stereo matching (and other image registration tasks) is the selection of optimal window sizes for comparing image regions. This paper addresses this problem with some novel algorithms based on iteratively diffusing support at different disparity hypotheses, and locally controlling the amount of diffusion based on the current quality of the disparity estimate. It also develops a novel Bayesian estimation technique, which significantly outperforms techniques based on area-based matching (SSD) and regular diffusion. We provide experimental results on both synthetic and real stereo image pairs.
引用
收藏
页码:155 / 174
页数:19
相关论文
共 74 条
[1]  
Barnard S.T.(1989)Stochastic stereo matching over scale International Journal of Computer Vision 3 17-32
[2]  
Barnard S.T.(1982)Computational stereo ACM Computing Surveys 14 553-572
[3]  
Fischler M.A.(1986)On the statistical analysis of dirty pictures Journal of the Royal Statistical Society B-48 259-302
[4]  
Besag J.(1996)On the unification of line processes, outlier rejection, and robust statistics with applications in early vision International Journal of Computer Vision 19 57-91
[5]  
Black M.J.(1987)Epipolarplane image analysis: An approach to determining structure from motion International Journal of Computer Vision 1 7-55
[6]  
Rangarajan A.(1989)Structure from stereo—A review IEEE Transactions on Systems, Man, and Cybernetics 19 1489-1510
[7]  
Bolles R.C.(1993)A parallel stereo algorithm that produces dense depth maps and preserves image features Machine Vision and Applications 6 35-49
[8]  
Baker H.H.(1991)Mean field theory for surface reconstruction IEEE Transactions on Pattern Analysis and Machine Intelligence 13 401-412
[9]  
Marimont D.H.(1984)Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images IEEE Transactions on Pattern Analysis and Machine Intelligence 6 721-741
[10]  
Dhond U.R.(1985)Computational experiments with a feature based stereo algorithm IEEE Transactions on Pattern Analysis and Machine Intelligence 7 17-34