A dual-adaptive support-based stereo matching algorithm

Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.

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

[3]  Zheng Zhi.  A Region Based Stereo Matching Algorithm Using Cooperative Optimization , 2009 .

[4]  Joost van de Weijer,et al.  Accurate Stereo Matching by Two-Step Energy Minimization , 2015, IEEE Transactions on Image Processing.

[5]  Xing Mei,et al.  On building an accurate stereo matching system on graphics hardware , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[6]  Zhaoqi Wang,et al.  3D entity-based stereo matching with ground control points and joint second-order smoothness prior , 2014, The Visual Computer.

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

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

[9]  Cheng Zhang,et al.  Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

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

[11]  Vamshhi Pavan Kumar Varma Vegeshna,et al.  Stereo Matching with Color-Weighted Correlation, Hierachical Belief Propagation and Occlusion Handling , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Federico Tombari,et al.  Segmentation-Based Adaptive Support for Accurate Stereo Correspondence , 2007, PSIVT.

[13]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[14]  Petros Daras,et al.  Enhanced disparity estimation in stereo images , 2015, Image Vis. Comput..

[15]  Neil A. Dodgson,et al.  Real-Time Spatiotemporal Stereo Matching Using the Dual-Cross-Bilateral Grid , 2010, ECCV.

[16]  Qingxiong Yang,et al.  Near Real-time Stereo for Weakly-Textured Scenes , 2008, BMVC.

[17]  Rudy Lauwereins,et al.  Real-time accurate stereo with bitwise fast voting on CUDA , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[18]  M. Bleyer,et al.  Near Real-Time Stereo With Adaptive Support Weight Approaches , 2010 .

[19]  Jian Sun,et al.  Parallel graph-cuts by adaptive bottom-up merging , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Franz Franchetti,et al.  High Performance Stereo Vision Designed for Massively Data Parallel Platforms , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

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

[22]  Margrit Gelautz,et al.  Local stereo matching using geodesic support weights , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[23]  Xing Mei,et al.  Stereo Matching with Reliable Disparity Propagation , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.