[1] 张永祥, 古佩强, 穆铁英. 改进的RANSAC基础矩阵估计算法[J]. 小型微型计算机系统, 2016, 37(9): 2084-2087.

    Zhang Y X, Gu P Q, Mu T Y. Improved RANSAC algorithm for fundamental matrix estimation[J]. Journal of Chinese Computer Systems, 2016, 37(9): 2084-2087.

[2] 黄以君, 刘伟军. 基于LQS的基本矩阵计算方法[J]. 中国图象图形学报, 2009, 14(10): 2069-2073.

    Huang Y Z, Liu W J. A method for fundamental matrix estimation using LQS[J]. Journal of Image and Graphics, 2009, 14(10): 2069-2073.

[3] 薛俊诗, 舒奇泉, 郭宁博. 未知畸变参数时多视图三维重建相对位姿估计方法[J]. 光子学报, 2018, 47(6): 0612002.

    Xue J S, Shu Q Q, Guo N B. Relative pose estimation method in multi-view 3D reconstruction with unknown distortion[J]. Acta Photonica Sinica, 2018, 47(6): 0612002.

[4] TaiC, Liu YH. Robust structure and motion estimation by auto-scale random sample consensus[C]∥2006 IEEE International Conference on Information Acquisition, August 20-23, 2006, Weihai, China. New York: IEEE, 2006: 37- 42.

[5] Zou Y X, Chan S C, Ng T S. Least mean M-estimate algorithms for robust adaptive filtering in impulse noise[J]. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 2000, 47(12): 1564-1569.

[6] Rousseeuw P J. Least median of squares regression[J]. Journal of the American Statistical Association, 1984, 79(388): 871-880.

[7] 王琼, 李言, 任伟建, 等. 图像三维重构中基于混合算法的基础矩阵估计[J]. 自动化技术与应用, 2017, 36(7): 1-6, 12.

    Wang Q, Li Y, Ren W J, et al. Fundamental matrix estimation based on improved hybrid algorithm in image 3D reconstruction[J]. Techniques of Automation and Applications, 2017, 36(7): 1-6, 12.

[8] 颜坤, 刘恩海, 赵汝进, 等. 快速鲁棒的基础矩阵估计[J]. 光学精密工程, 2018, 26(2): 461-470.

    Yan K, Liu E H, Zhao R J, et al. A fast and robust method for fundamental matrix estimation[J]. Optics and Precision Engineering, 2018, 26(2): 461-470.

[9] 鲁珊, 雷英杰, 孔韦韦, 等. 基于模糊核聚类的鲁棒性基础矩阵估计算法[J]. 吉林大学学报(工学版), 2012, 42(2): 434-439.

    Lu S, Lei Y J, Kong W W, et al. Robust fundamental matrix estimation based on kernel fuzzy clustering[J]. Journal of Jilin University(Engineering and Technology Edition), 2012, 42(2): 434-439.

[10] Chum O, Matas J. Optimal randomized RANSAC[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(8): 1472-1482.

[11] ChumO, MatasJ, KittlerJ. Local lyoptimized RANSAC[M] ∥Michaelis B, Krell G. Pattern recognition. DAGM 2003. Lecture notes in computer science. Berlin, Heidelberg: Springer, 2003, 2781: 236- 243.

[12] 董安国, 李佳逊, 张蓓, 等. 基于谱聚类和稀疏表示的高光谱图像分类算法[J]. 光学学报, 2017, 37(8): 0828005.

    Dong A G, Li J X, Zhang B, et al. Hyperspectral image classification algorithm based on spectral clustering and sparse representation[J]. Acta Optica Sinica, 2017, 37(8): 0828005.

[13] 周世波, 徐维祥. 一种基于相对密度和决策图的聚类算法[J]. 控制与决策, 2018, 33(11): 1921-1930.

    Zhou S B, Xu W X. A novel clustering algorithm based on relative density and decision graph[J]. Control and Decision, 2018, 33(11): 1921-1930.

[14] 曾台英, 杜菲. 基于层次聚类的图像超分辨率重建[J]. 光学学报, 2018, 38(4): 0410004.

    Zeng T Y, Du F. Image super-resolution reconstruction based on hierarchical clustering[J]. Acta Optica Sinica, 2018, 38(4): 0410004.

[15] 赵凯, 徐友春, 李永乐, 等. 基于VG-DBSCAN算法的大场景散乱点云去噪[J]. 光学学报, 2018, 38(10): 1028001.

    Zhao K, Xu Y C, Li Y L, et al. Large-scale scattered point-cloud denoising based on VG-DBSCAN algorithm[J]. Acta Optica Sinica, 2018, 38(10): 1028001.

[16] Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.

[17] 韩利钊, 钱雪忠, 罗靖, 等. 基于区域划分的DBSCAN多密度聚类算法[J]. 计算机应用研究, 2018, 35(6): 1668-1671, 1685.

    Han L Z, Qian X Z, Luo J, et al. Multi-density clustering algorithm DBSCAN based on region division[J]. Application Research of Computers, 2018, 35(6): 1668-1671, 1685.

[18] 李静, 杨宜民, 张学习. 一种改进的MLESAC基本矩阵估计算法[J]. 计算机工程, 2012, 38(19): 214-217.

    Li J, Yang Y M, Zhang X X. Animproved MLESAC algorithm for estimating fundamental matrix[J]. Computer Engineering, 2012, 38(19): 214-217.

[19] 向长波, 刘太辉. 基本矩阵的鲁棒贪心估计算法[J]. 计算机辅助设计与图形学学报, 2007, 19(5): 651-655.

    Xiang C B, Liu T H. A robust greedy algorithm for estimating the fundamental matrix[J]. Journal of Computer-Aided Design & Computer Graphics, 2007, 19(5): 651-655.

[20] 王洋, 张桂珠. 自动确定聚类中心的密度峰值算法[J]. 计算机工程与应用, 2018, 54(8): 137-142.

    Wang Y, Zhang G Z. Automatically determine density of cluster center of peak algorithm[J]. Computer Engineering and Applications, 2018, 54(8): 137-142.

[21] Hartley R I. In defense of the eight-point algorithm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(6): 580-593.

[22] 李言. 基于未标定图像的油田地面设备三维重建研究与实现[D]. 大庆: 东北石油大学, 2016.

    LiY. Research and implementation on 3D reconstruction about oilfield ground equipment of uncalibrated image[D]. Daqing: Northeast Petroleum University, 2016.