Graph-Cut and Belief-Propagation Stereo on Real-World Image Sequences

This paper deals with stereo correspondence search, using graph cuts and belief propagation, for estimating depth maps. The results following different preprocessing steps are evaluated, using the quality of the disparity map. Running times are also investigated. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury Stereo website, synthetic driving scenes, and real-world stereo sequences, either provided by Daimler AG or self-recorded with the research vehicle of the .enpeda.. project at Auckland University.

[1]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[3]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[4]  Reinhard Klette,et al.  Belief Propagation for Stereo Analysis of Night-Vision Sequences , 2009, PSIVT.

[5]  Pedro F. Felzenszwalb,et al.  Efficient belief propagation for early vision , 2004, CVPR 2004.

[6]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Reinhard Klette,et al.  Belief-Propagation on Edge Images for Stereo Analysis of Image Sequences , 2008, RobVis.

[8]  Vladimir Kolmogorov,et al.  Graph Cut Algorithms for Binocular Stereo with Occlusions , 2006, Handbook of Mathematical Models in Computer Vision.

[9]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  P. J. Narayanan,et al.  CUDA cuts: Fast graph cuts on the GPU , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.