Colour-weighted rank transform and improved dynamic programming for fast and accurate stereo matching

References

• Hamzah RA, Ibrahim H. Literature survey on stereo vision disparity map algorithms. J Sens. 2016;2016:1–23.
   Web of Science ®Google Scholar
• Barnes C, Zhang FL. A survey of the state-of-the-art in patch-based synthesis. Comput Vis Media. 2017;3(1):3–20.
   Google Scholar
• Einecke N, Eggert J. A two-stage correlation method for stereoscopic depth estimation. 2010 International Conference on Digital Image Computing: Techniques and Applications, Sydney, NSW, Australia, 2010. p. 227–234. Sydney, NSW, Australia: IEEE.
   Google Scholar
• Lu H, Xu H, Zhang L, et al. Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching. IEEE visual Communications and Image Processing (VCIP). 2018. p. 1–4
   Google Scholar
• Hirschmuller H, Scharstein D. Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans Pattern Anal Mach Intell. 2009;31(9):1582–1599.
   PubMed Web of Science ®Google Scholar
• Liang Q, Yang Y, Liu B. Stereo matching algorithm based on ground control points using graph cut. 2014 7th International Congress on Image and Signal Processing. IEEE; 2014. p. 503–508.
   Google Scholar
• Wang L, Liao M, Gong M, et al. High-quality real-time stereo using adaptive cost aggregation and dynamic programming. Third International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’06). IEEE. 2006. p. 798–805.
   Google Scholar
• Hirschmuller H. Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell. 2008;30(2):328–341.
   PubMed Web of Science ®Google Scholar
• Hamzah RA, Ibrahim H, Hassan AHA. Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. J Vis Commun Image Represent. 2017;42:145–160.
   Web of Science ®Google Scholar
• Hallek, M., Boukamcha, H., Mtibaa, A. et al. Dynamic programming with adaptive and self-adjusting penalty for real-time accurate stereo matching. J Real-Time Image Proc. 2022;19:233–245.
   Google Scholar
• Zhu S, Yan L. Local stereo matching algorithm with efficient matching cost and adaptive guided image filter. Vis Comput. 2017;33(9):1087–1102.
   Web of Science ®Google Scholar
• Kordelas GA, Alexiadis DS, Daras P, et al. Enhanced disparity estimation in stereo images. Image Vis Comput. 2015;35:31–49.
   Web of Science ®Google Scholar
• Hosni A, Rhemann C, Bleyer M, et al. Fast cost volume filtering for visual correspondence and beyond. IEEE Trans Pattern Anal Mach Intell. 2013;35(2):504–511.
   PubMed Web of Science ®Google Scholar
• Hosni A, Bleyer M, Rhemann C, et al. Real-time local stereo matching using guided image filtering. 2011 IEEE International Conference on Multimedia and Expo. 2011. p. 1–6.
   Google Scholar
• Scharstein D, Szeliski R. A taxonomy and evaluation of dense two frame stereo correspondence algorithms. Int J Comput Vision. 2002;47(1):7–42.
   Web of Science ®Google Scholar
• Kendall A, Martirosyan H, Dasgupta S, et al. End-to-end learning of geometry and context for deep stereo regression. Proceedings of the IEEE International Conference on Computer Vision. Venice. 2017. p. 66–75.
   Google Scholar
• Li X, Fan Y, Lv G, et al. Area-based correlation and non-local attention network for stereo matching. Vis Comput. 2022;38:3881–3895.
   Web of Science ®Google Scholar
• Wang Q, Shi S, Zheng S, et al. Fadnet: A fast and accurate network for disparity estimation. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020. p. 101–107.
   Google Scholar
• Zheng GW, Jiang XH. A fast stereo matching algorithm based on fixed window. In: Applied mechanics and materials. Trans Tech. 2013;411–414:1305–1313.
   Google Scholar
• Hirschmüller H, Innocent PR, Garibaldi J. Real-time correlation based stereo vision with reduced border errors. Int J Comput Vision. 2002;47(1):229–246.
   Web of Science ®Google Scholar
• Zabih R, Woodfill J. Non-parametric local transforms for computing visual correspondence. Proceedings of the Third European Conference on Computer Vision. Stockholm; 1994. p. 151–158.
   Google Scholar
• Wang K. Adaptive stereo matching algorithm based on edge detection. 2004 International Conference on Image Processing, ICIP’04. 2, IEEE. 2004. p. 1345–1348.65.
   Google Scholar
• Gu Z, Su X, Liu Y, et al. Local stereo matching with adaptive support-weight,: rank transform and disparity calibration. Pattern Recognit Lett. 2008;29(9):1230–1235.
   Web of Science ®Google Scholar
• Demetz O, Hafner D, Weickert J. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC. 2013.
   Google Scholar
• Mei X, Sun X, Zhou M, et al. On building an accurate stereo matching system on graphics hardware. IEEE international Conference on Computer Vision Workshops (ICCV 74 Workshops). 2011. p. 467–474.
   Google Scholar
• Tan P, Monasse P. Stereo disparity through cost aggregation with guided filter. Image Process Line. 2014;4:252–275.
   Google Scholar
• Yang Q, Ji P, Li D, et al. Fast stereo matching using adaptive guided filtering. Image Vis Comput. 2014;32(3):202–211.
   Web of Science ®Google Scholar
• Zhan Y, Gu Y, Huang K, et al. Accurate image guided stereo matching with efficient matching cost and disparity refinement. IEEE Trans Circuits Syst Video Technol. 2016;26(9):1632–1645.
   Web of Science ®Google Scholar
• Wang L, Yang R, Gong M, et al. Real-time stereo using approximated joint bilateral filtering and dynamic programming. J Real-Time Image Process. 2014;9(3):447–461.
   Web of Science ®Google Scholar
• Hamzah R, Hamid M, Kadmin A, et al. Disparity map algorithm based on edge preserving filter for stereo video processing. J Telecommun Electron Comput Engg. 2018;10(1–7):59–62.
   Google Scholar
• Zhu S, Wang Z, Zhang X, et al. Edge-preserving guided filtering based cost aggregation for stereo matching. J Vis Commun Image Represent. 2016;39:107–119.
   Web of Science ®Google Scholar
• Dong Q, Feng J. Adaptive disparity computation using local and non-local cost aggregations. Multimed Tools Appl. 2018;77(24):31647–31663.
   Web of Science ®Google Scholar
• Wu W, Zhu H, Yu S, et al. Stereo matching with fusing adaptive support weights. IEEE Access. 2019;7:61960–61974.
   Web of Science ®Google Scholar
• Lei C, Selzer J, Yang YH. Region-tree based stereo using dynamic programming optimization. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). 2, 2006. p. 2378–2385.
   Google Scholar
• Kim JC, Lee KM, Choi BT, et al. A dense stereo matching using two-pass dynamic programming with generalized ground control points. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). 2, 2005. p. 1075–1082.
   Google Scholar
• Chen F, Liu X, Yu H, et al. Clif: cross-layer information fusion for stereo matching and its hardware implementation. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). 2021. pp. 1–5.
   Google Scholar
• Cambuim LF, Oliveira LA, Barros EN, et al. An fpga-based real-time occlusion robust stereo vision system using semi-global matching. J Real-Time Image Process. 2020;17(5):1447–1468.
   Web of Science ®Google Scholar
• Chang Q, Maruyama T. Real-time stereo vision system: a multi-block matching on gpu. IEEE Access. 2018;6:42030–42046.
   Web of Science ®Google Scholar
• Hallek M, Smach F, Atri M. Real-time stereo matching on CUDA using Fourier descriptors and dynamic programming. Comput Vis Media. 2019;5(1):59–71.
   Google Scholar
• Cox IJ, Hingorani SL, Rao SB, et al. A maximum likelihood stereo algorithm. Comput Vis Image Underst. 1996;63(3):542–567.
   Web of Science ®Google Scholar
• Jiao J, Wang R, Wang W, et al. Local stereo matching with improved matching cost and disparity refinement. IEEE MultiMedia. 2014;21(4):16–27.
   Web of Science ®Google Scholar
• Ma Z, He K, Wei Y, et al. Constant time weighted median filtering for stereo matching and beyond. Proceedings of the IEEE International Conference on Computer Vision. 2013. p. 49–56.
   Google Scholar
• He K, Sun J, Tang X. Guided image filtering. IEEE Trans Pattern Anal Mach Intell. 2013;35(6):1397–1409.
   PubMed Web of Science ®Google Scholar
• Scharstein D, Hirschmüller H, Kitajima Y, et al. High-resolution stereo datasets with subpixel-accurate ground truth. German Conference on Pattern Recognition. 2014. pp. 31–42.
   Google Scholar
• Menze M, Geiger A. Object scene flow for autonomous vehicles. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. p. 3061–3070.
   Google Scholar
• Kowalczuk J, Psota ET, Perez LC. Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences. IEEE Trans: Circuits Syst Video Technol. 2013;23(1):94–104.
   Web of Science ®Google Scholar
• Mozerov MG, Van De Weijer J. Accurate stereo matching by two-step energy minimization. IEEE Trans Image Process. 2015;24(3):1153–1163.
   PubMed Web of Science ®Google Scholar
• Yin J, Zhu H, Yuan D, et al. Sparse representation over discriminative dictionary for stereo matching. Pattern Recognit. 2017;71:278–289.
   Web of Science ®Google Scholar
• Hu Y, Zhen W, Scherer S. Deep-learning assisted high-resolution binocular stereo depth reconstruction. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020. p. 8637–8643.
   Google Scholar
• Chang Q, Zha A, Wang W, et al. Efficient stereo matching on embedded GPUs with zero-means cross correlation. J Syst Archit. 2022;123:102366.
   Web of Science ®Google Scholar
• Hirner D, Fraundorfer F. FC-DCNN: A densely connected neural network for stereo estimation. 2020 25th International Conference on Pattern Recognition (ICPR). IEEE; 2021. p. 2482–2489.
   Google Scholar
• Boitumelo R, Jonas M, Martin W, et al. ReS2tAC-UAV-borne real-time SGM stereo optimized for embedded ARM and CUDA devices. MDPI Sensors. 2021;21(11):1–37.
   Web of Science ®Google Scholar