A Global Sparse Stereo Matching Method under Structure Tensor Constraint

In this paper, a global algorithm based on graph cuts theory is proposed to solve the sparse stereo matching problem. The sparse feature points are extracted by the Harris corner detector. The matching problem is transformed into a labeling problem in the sparse graph which can be solved by energy minimization. In this algorithm, the graph is constructed by sparse feature points instead of pixels, which can lead to simple graph structure. In addition, a structure tensor descriptor, which is invariant to varying illumination, is used as similarity measurement to obtain more accurate result. The experimental results show that this algorithm can obtain accurate matching result.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

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

[3]  Olga Veksler,et al.  Semi-dense stereo correspondence with dense features , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[4]  Michel Devy,et al.  Stereo Matching using Reduced-Graph Cuts , 2007, 2007 IEEE International Conference on Image Processing.

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

[6]  João Paulo Costeira,et al.  A Global Solution to Sparse Correspondence Problems , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jen-Hui Chuang,et al.  Finding Point Correspondence Using Local Similarity and Global Constraint , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[8]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Peter H. N. de With,et al.  Contrast-Invariant Feature Point Correspondence , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.