An improved graph cut algorithm in stereo matching

Abstract In stereo matching tasks, the matching effect is often very poor when the texture of the region is weak or repeated. To solve this problem, an improved Graph Cut stereo matching algorithm based on Census transform is proposed. The Hamming distance of the corresponding pixels in the left and right images after Census transform is introduced as the similarity measure in the data term of the energy function. In this way, the dependence on the pixel value is reduced. The stereo matching experiments are implemented on the standard images of Middlebury stereo benchmark and the real scene images, and it demonstrates that our algorithm is robust and can obtain better performance in weak texture or repeated texture region.

[1]  Chang Wook Ahn,et al.  Parameter selection framework for stereo correspondence , 2020, Machine Vision and Applications.

[2]  Lu Zhang,et al.  Multi‐view frontal face image generation: A survey , 2020, Concurr. Comput. Pract. Exp..

[3]  Guorong Sui,et al.  An efficient local stereo matching method based on an adaptive exponentially weighted moving average filter in SLIC space , 2021, IET Image Process..

[4]  Aaron F. Bobick,et al.  Large Occlusion Stereo , 1999, International Journal of Computer Vision.

[5]  Yann LeCun,et al.  Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches , 2015, J. Mach. Learn. Res..

[6]  Guijin Wang,et al.  High-Accuracy Stereo Matching Based on Adaptive Ground Control Points , 2015, IEEE Transactions on Image Processing.

[7]  Shengming Zhang,et al.  Fixed window aggregation AD-census algorithm for phase-based stereo matching. , 2019, Applied optics.

[8]  Heiko Hirschmüller,et al.  Evaluation of Cost Functions for Stereo Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Chen Wang,et al.  Self-Supervised deep homography estimation with invertibility constraints , 2019, Pattern Recognit. Lett..

[11]  Byung-Gyu Kim,et al.  A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range images , 2017, Displays.

[12]  Richard Szeliski,et al.  Efficient High-Resolution Stereo Matching Using Local Plane Sweeps , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  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).

[14]  Gonzalo Pajares,et al.  Combining Support Vector Machines and simulated annealing for stereovision matching with fish eye lenses in forest environments , 2011, Expert Syst. Appl..

[15]  Wenjie Liu,et al.  A Fast Single Image Haze Removal Method Based on Human Retina Property , 2017, IEICE Trans. Inf. Syst..

[16]  Michael Goesele,et al.  Multi-frame stereo matching with edges, planes, and superpixels , 2019, Image Vis. Comput..

[17]  Jun Zhou,et al.  Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching , 2020, AAAI.

[18]  Hegao Cai,et al.  Local‐global stereo matching algorithm , 2006 .

[19]  Walid Mahdi,et al.  Performance Analysis of Simulated Annealing Cooling Schedules in the Context of Dense Image Matching , 2017, Computación y Sistemas.

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

[21]  Raquel Urtasun,et al.  Efficient Deep Learning for Stereo Matching , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Ke Gong,et al.  Feature Refinement and Filter Network for Person Re-Identification , 2021, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Jun Zhou,et al.  Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss , 2020, IEEE Transactions on Multimedia.

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

[25]  Jie Wang,et al.  Learning efficient multi-task stereo matching network with richer feature information , 2021, Neurocomputing.

[26]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[27]  Saeed Mahmoudpour,et al.  The effect of depth map up-sampling on the overall quality of stereopairs , 2016, Displays.

[28]  Guangtao Zhai,et al.  Extended geometric models for stereoscopic 3D with vertical screen disparity , 2020, Displays.

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

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

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

[32]  Jin Wang,et al.  Lightweight deep network for traffic sign classification , 2019, Annals of Telecommunications.

[33]  Yu-Jin Zhang,et al.  An O(1) disparity refinement method for stereo matching , 2016, Pattern Recognit..

[34]  Lingli Zhan,et al.  A weighting algorithm based on the gravitational model for local stereo matching , 2020, Signal Image Video Process..

[35]  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).

[36]  Jiang Xu,et al.  The Principle of Homology Continuity and Geometrical Covering Learning for Pattern Recognition , 2018, Int. J. Pattern Recognit. Artif. Intell..

[37]  Jun Zhou,et al.  Adaptive hash retrieval with kernel based similarity , 2018, Pattern Recognit..

[38]  Pengfei Duan,et al.  Real-Time 3D Face Alignment Using an Encoder-Decoder Network With an Efficient Deconvolution Layer , 2020, IEEE Signal Processing Letters.

[39]  Weijun Li,et al.  GmFace: An explicit function for face image representation , 2021, Displays.

[40]  Xianglong Liu,et al.  Self-Supervised Multiscale Adversarial Regression Network for Stereo Disparity Estimation , 2020, IEEE Transactions on Cybernetics.

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

[42]  Xin Yu,et al.  3D cost aggregation with multiple minimum spanning trees for stereo matching. , 2017, Applied optics.

[43]  Sergiu Nedevschi,et al.  Optimizing Census-based Semi Global Matching by genetic algorithms , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[44]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[45]  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).