Abstract Aiming at the problems of the noise and illumination sensitive and with high false matching radio in the weak texture region of the local matching algorithm, an robust stereo matching algorithm for improved Census transform and adaptive parameter-guided filter is proposed. The hue, saturation, value(HSV) channel value is used to calculate the cost, which has higher robustness to noise and illumination changes. In the support window, the distance weighting and color weighting are used to obtain the reference value of the census transform respectively. The two costs are fused to improve the stability of the single pixel matching cost. The kurtosis adaptive regularization parameter is used on the guide filter for cost aggregation. Finally, the disparity map is obtained by disparity calculation. The matching experiment is carried out on the image pairs provided by Middlebury database on the VS2017. The average error matching rate of the proposed algorithm of standard image, noisy image and illumination change image is 7.80%, 10.72% and 9.89% respectively. The results show that the algorithm can reduce the false matching ratio and better adapt to the changes of noise and illumination.
Zhang Y H, Xu B R, Zhu J J, et al. 3D Temperature Model Reconstruction Based on Fusion of Visible and Thermal Images. Acta Metrologica Sinica［J］, 2022, 43(2): 256-263.
Wang J M, Qiao W D, Zhang H, et al. Dynamic Glare Measurement Method Based on Threshold Increment. Acta Metrologica Sinica［J］, 2022, 43(4): 475-481.
［11］
Zabih R, Woodfill J. Non-parametric local transforms for computing visual correspondence［C］//European Conference on Computer Vision. Berlin, Germany, 1994.
Li H X, Li W L. Binocular Vision 3D Reconstruction Based on Deformable Convolution. Acta Metrologica Sinica［J］, 2022, 43(6): 737-745.
［4］
Scharstein D, Szeliski R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms［J］. International Journal of Computer Vision, 2002, 47(1): 7-42.
［6］
Besse F, Rother C, Fitzgibbon A, et al. PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation［J］. International Journal of Computer Vision, 2014, 110(1): 2-13.
［7］
Worby J, Maclean W J. Establishing Visual Correspondence from Multi-Resolution Graph Cuts for Stereo-Motion［C］//IEEE Fourth Canadian Conference on Computer and Robot Vision. Montreal, Canada, 2007.
［9］
Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector［C］//European conference on computer vision. Amsterdam, Netherlands, 2016: 21-37.
Li H B, Dong Y, Li Y Q. A Combined Cost Algorithm Based on Adaptive Parameter［J］. Acta Metrologica Sinica , 2017, 38(3): 280-283.
［15］
He K, Sun J, Tang X. Guided image filtering［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
［16］
Pauline T, Pascal M. Stereo Disparity through Cost Aggregation with Guided Filter［J］. Image Processing On Line, 2014, 4: 252-275.
Sun J, Zheng N N, Shum H Y. Stereo matching using belief propagation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 787-800.
［8］
Kanade T, Okutomi M. A stereo matching algorithm with an adaptive window: theory and experiment［J］. IEEE Transactions on Pattern Analysis Machine Intelligence, 1994, 16(9): 920-932.
［10］
Hirschmüller H, Innocent P R, Garibaldi J. Real-Time Correlation-Based Stereo Vision with Reduced Border Errors［J］. International Journal of Computer Vision, 2002, 47(1): 229-246.
［12］
Mei X, Sun X, Zhou M, et al. On building an accurate stereo matching system on graphics hardware［C］//IEEE International Conference on Computer Vision Workshops. Barcelona, Espana, 2011: 467-474.
［14］
Yoon K J, KweonI S. Adaptive support-weight approach for correspondence search［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 650-656.
［17］
Chang Y C, Tsai T H, Hsu B H, et al. Algorithm and Architecture of Disparity Estimation With Mini-Census Adaptive Support Weight［J］. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(6): 792-805.
Fan H R, Yang F, Pan X R, et al. Stereo Matching Algorithm for Improved Census Transform and Gradient Fusion［J］. Acta Optica Sinica,2018, 38(2): 260-270.
Fang H P, Zeng R Y, Wu J X, et al. Weighted Adaptive Guided Filtering Based on Kurtosis Variance［J］. Transactions of Beijing Institute of Technology, 2021, 41(11): 1193-1200.
2023, Vol. 44 
