Efficient methods using slanted support windows for slanted surfaces

The frontal-parallel assumption is made by many matching algorithms, but this assumption fails for slanted surfaces. This study proposes a matching algorithm intended to improve the matching results for slanted surfaces. First, a mathematical model is constructed to prove that slanted surfaces in the environment have corresponding slanted disparity surfaces in the disparity space image, and the model is to help find the proper plane parameters of slanted support windows, then improved cost aggregation and post-processing methods are proposed. The algorithm is tested using the Middlebury and Karlsruhe Institute of Technology and Toyota Technical Institute at Chicago (KITTI) benchmarks. The results demonstrate that the algorithm exhibits good performance and is efficient for slanted surfaces.

[1]  Mongi A. Abidi,et al.  Occlusion filling in stereo: Theory and experiments , 2013, Comput. Vis. Image Underst..

[2]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Alain Crouzil,et al.  Similarity measures for image matching despite occlusions in stereo vision , 2011, Pattern Recognit..

[4]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Margrit Gelautz,et al.  Secrets of adaptive support weight techniques for local stereo matching , 2013, Comput. Vis. Image Underst..

[6]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Gauthier Lafruit,et al.  Cross-Based Local Stereo Matching Using Orthogonal Integral Images , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Alexandrina Rogozan,et al.  A robust cost function for stereo matching of road scenes , 2014, Pattern Recognit. Lett..

[9]  Minglun Gong,et al.  Near-real-time stereo matching with slanted surface modeling and sub-pixel accuracy , 2011, Pattern Recognit..

[10]  D. Nistér,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  C. Stentoumis,et al.  On accurate dense stereo-matching using a local adaptive multi-cost approach , 2014 .

[12]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

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