An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space

[1]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

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

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

[4]  Shengyong Chen,et al.  MSCS: MeshStereo with Cross-Scale Cost Filtering for fast stereo matching , 2018, IET Comput. Vis..

[5]  Xi Wang,et al.  High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth , 2014, GCPR.

[6]  Carsten Rother,et al.  Fast Cost-Volume Filtering for Visual Correspondence and Beyond , 2013, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  [Influential Observations, High Leverage Points, and Outliers in Linear Regression]: Comment , 1986 .

[8]  Liang-Gee Chen,et al.  Accurate and fast segment-based cost aggregation algorithm for stereo matching , 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).

[9]  Yong Jin,et al.  Regional fuzzy binocular stereo matching algorithm based on global correlation coding for 3D measurement of rail surface , 2020 .

[10]  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).

[11]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Hua Liu,et al.  Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images , 2020, Applied Sciences.

[13]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Yongjun Zhang,et al.  IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION , 2016 .

[15]  Haidi Ibrahim,et al.  Improvement of stereo matching algorithm for 3D surface reconstruction , 2018, Signal Process. Image Commun..

[16]  Daoping Huang,et al.  Novel Belief Propagation Algorithm for Stereo Matching With a Robust Cost Computation , 2019, IEEE Access.

[17]  In-So Kweon,et al.  Support Aggregation via Non-linear Diffusion with Disparity-Dependent Support-Weights for Stereo Matching , 2009, ACCV.

[18]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[19]  Wei Chen,et al.  Learning Deep Correspondence through Prior and Posterior Feature Constancy , 2017, ArXiv.

[20]  Thomas Brox,et al.  A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Yan Zhou,et al.  Texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization for stereo matching , 2019, J. Electronic Imaging.

[22]  Sangyoon Lee,et al.  Near-real-time stereo matching method using both cross-based support regions in stereo views , 2018 .

[23]  Theocharis Theocharides,et al.  High-quality real-time hardware stereo matching based on guided image filtering , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[24]  Yanbing Xue,et al.  Iterative color-depth MST cost aggregation for stereo matching , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[25]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..

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

[27]  Jianguo Liu,et al.  Efficient methods using slanted support windows for slanted surfaces , 2016 .

[28]  Enric Meinhardt,et al.  MGM: A Significantly More Global Matching for Stereovision , 2015, BMVC.

[29]  Lifeng Sun,et al.  Cross-Scale Cost Aggregation for Stereo Matching , 2014, CVPR.

[30]  Ding Yuan,et al.  SVCV: segmentation volume combined with cost volume for stereo matching , 2017, IET Comput. Vis..

[31]  Qiong Yan,et al.  Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[32]  Yann LeCun,et al.  Computing the stereo matching cost with a convolutional neural network , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Yangzhou Gan,et al.  Segment-Based Disparity Refinement With Occlusion Handling for Stereo Matching , 2019, IEEE Transactions on Image Processing.

[34]  Yau-Zen Chang,et al.  Stereo matching for infrared images using guided filtering weighted by exponential moving average , 2020, IET Image Process..

[35]  Reinhard Klette,et al.  Iterative Semi-Global Matching for Robust Driver Assistance Systems , 2012, ACCV.

[36]  Takeshi Naemura,et al.  Continuous 3D Label Stereo Matching Using Local Expansion Moves , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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