Hierarchical stereo matching with image bit-plane slicing

We propose a new stereo matching framework based on image bit-plane slicing. A pair of image sequences with various intensity quantization levels constructed by taking different bit-rate of the images is used for hierarchical stereo matching. The basic idea is to use the low bit-rate image pairs to compute rough disparity maps. The hierarchical matching strategy is then carried out iteratively to update the low confident disparities with the information provided by extra image bit-planes. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Consequently, variate bit-rate matching is performed only regionally in the images for each iteration, and the average image bit-rate for disparity computation is reduced. Our method provides a hierarchical matching framework and can be combined with the existing stereo matching algorithms. Experiments on Middlebury datasets show that the proposed technique gives good results compared to the conventional full bit-rate matching.

[1]  Andreas Klaus,et al.  Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Stefan K. Gehrig,et al.  A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching , 2009, ICVS.

[5]  Yi-Ping Hung,et al.  Fast block matching algorithm based on the winner-update strategy , 2001, IEEE Trans. Image Process..

[6]  Shree K. Nayar,et al.  Ordinal Measures for Image Correspondence , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Vladimir Kolmogorov,et al.  Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Andrew Witkin,et al.  Signal matching through scale space , 1986, AAAI 1986.

[9]  Jake K. Aggarwal,et al.  Structure from stereo-a review , 1989, IEEE Trans. Syst. Man Cybern..

[10]  Ruigang Yang,et al.  Multi-resolution real-time stereo on commodity graphics hardware , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[12]  Kwanghoon Sohn,et al.  Cost Aggregation and Occlusion Handling With WLS in Stereo Matching , 2008, IEEE Transactions on Image Processing.

[13]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Liang Zhang.  Fast Stereo Matching Algorithm for Intermediate View Reconstruction of Stereoscopic Television Images , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Gauthier Lafruit,et al.  Stream-Centric Stereo Matching and View Synthesis: A High-Speed Approach on GPUs , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Yee-Hong Yang,et al.  Multi-resolution stereo matching using genetic algorithm , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[17]  Markus Vincze,et al.  A fast stereo matching algorithm suitable for embedded real-time systems , 2010, Comput. Vis. Image Underst..

[18]  Ingemar J. Cox,et al.  A maximum-flow formulation of the N-camera stereo correspondence problem , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[19]  Martin A. Fischler,et al.  Computational Stereo , 1982, CSUR.

[20]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

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

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

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

[24]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[25]  Yi-Ping Hung,et al.  Multipass hierarchical stereo matching for generation of digital terrain models from aerial images , 1998, Machine Vision and Applications.

[26]  Carlo Tomasi,et al.  Depth Discontinuities by Pixel-to-Pixel Stereo , 1999, International Journal of Computer Vision.

[27]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..