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[1]何凯,闫佳星,魏颖,等.基于改进光流场模型的非刚性图像配准[J].天津大学学报(自然科学版),2018,(05):491-496.[doi:10.11784/tdxbz201705050]
 He Kai,Yan Jiaxing,Wei Ying,et al.Non-Rigid Image Registration Using Improved Optical Flow Field Model[J].Journal of Tianjin University,2018,(05):491-496.[doi:10.11784/tdxbz201705050]
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基于改进光流场模型的非刚性图像配准

参考文献/References:

[1] 杨爱萍, 侯正信, 王成优, 等. 基于全相位频谱分析的图像配准[J]. 天津大学学报, 2008, 41(12):1465-1472.
Yang Aiping, Hou Zhengxin, Wang Chengyou, et al. Image registration based on all phase spectrum analysis [J]. Journal of Tianjin University, 2008, 41(12):1465-1472(in Chinese).
[2] 张力新, 安会霞, 林旻, 等. 基于图像配准的CT定位像床板影校正[J]. 天津大学学报, 2006, 39(11):1375-1378.
Zhang Lixin, An Huixia, Lin Min, et al. Cradle image correction method for CT scout scan based on image registration[J]. Journal of Tianjin University, 2006, 39(11):1375-1378(in Chinese).
[3] Zitova B, Flusser J. Image registration methods:A survey[J]. Image and Vision Computing, 2003, 21(11):977-1000.
[4] 葛盼盼, 陈强, 顾一禾. 基于 Harris 角点和 SURF 特征的遥感图像匹配算法[J]. 计算机应用研究, 2014, 31(7):2205-2208.
Ge Panpan, Chen Qiang, Gu Yihe. Algorithm of remote sensing image matching based on Harris corner and SURF feature[J]. Application Research of Computers, 2014, 31(7):2205-2208(in Chinese).
[5] 李晖晖, 郑平, 杨宁, 等. 基于 SIFT 特征和角度相对距离的图像配准算法[J]. 西北工业大学学报, 2017, 35(2):280-285.
Li Huihui, Zheng Ping, Yang Ning, et al. Relative angle distance for image registration based on SIFT feature [J]. Journal of Northwestern Polytechnical University, 2017, 35(2):280-285(in Chinese).
[6] Horn B, Schunck B. Determining optical flow[J]. Artificial Intelligence, 1981, 17(2):185-203.
[7] Brox T, Bruhn A, Papenberg N, et al. High accuracy optical flow estimation based on a theory for warping [C]// Proceedings of the 2004 European Conference on Computer Vision. Berlin, Germany, 2004:25-36.
[8] Wedel A, Pock T, Zach C, et al. An improved algorithm for TV-L1 optical flow[J]. Ionics, 2009, 16(7):613-619.
[9] 韩雨, 王卫卫, 冯象初. 基于迭代重加权的非刚性图像配准[J]. 自动化学报, 2011, 37(9):1059-1066.
Han Yu, Wang Weiwei, Feng Xiangchu. Iteratively reweighted method based nonrigid image registration[J]. Acta Automatica Sinica, 2011, 37(9):1059-1066(in Chinese).
[10] 王婕妤, 王加俊, 张静亚. 基于改进光流场和尺度不变特征变换的非刚性医学图像配准[J]. 电子与信息学报, 2013, 35(5):1222-1228.
Wang Jieyu, Wang Jiajun, Zhang Jingya. Non-rigid medical image registration based on improved optical flow method and scale-invariant feature transform[J]. Journal of Electronics & Information Technology, 2013, 35(5):1222-1228(in Chinese).
[11] 陈震, 张聪炫, 晏文敬, 等. 基于图像局部结构的区域匹配变分光流算法[J]. 电子学报, 2015, 43(11):2200-2209.
Chen Zhen, Zhang Congxuan, Yan Wenjing, et al. Region matching variational optical flow algorithm based on image local structure[J]. Acta Electronica Sinica, 2015, 43(11):2200-2209(in Chinese).
[12] Amiaz T, Lubetzky E, Kiryati N. Coarse to over-fine optical flow estimation[J]. Pattern Recognition, 2007, 40(9):2496-2503.
[13] Xu L, Chen J, Jia J. A segmentation based variational model for accurate optical flow estimation[J]. Computer Vision, 2008, 5302:671-684.
[14] 梅广辉, 陈震, 危水根, 等. 图像光流联合驱动的变分光流计算新方法[J]. 中国图象图形学报, 2011, 16(12):2159-2168.
Mei Guanghui, Chen Zhen, Wei Shuigen, et al. New algorithm for estimation of variational optical flow with image-and flow-driven[J]. Journal of Image and Graphics, 2011, 16(12):2159-2168(in Chinese).
[15] Brox T, Malik J. Large displacement optical flow:Descriptor matching in variational motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3):500-513.
[16] 许鸿奎, 江铭炎, 杨明强. 基于改进光流场模型的脑部多模医学图像配准[J]. 电子学报, 2012, 40(3):525-529.
Xu Hongkui, Jiang Mingyan, Yang Mingqiang. Registration of multimodal brain medical images based on improved optical flow model[J]. Acta Electronica Sinica, 2012, 40(3):525-529(in Chinese).
[17] Chen Z, Jin H, Lin Z, et al. Large displacement optical flow from nearest neighbor fields [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013:2443-2450.
[18] Bao L, Yang Q, Jin H. Fast edge-preserving patch match for large displacement optical flow [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014:3534-3541.
[19] Sun D, Roth S, Lewis J P, et al. A quantitative analysis of current practices in optical flow estimation and the principles behind them[J]. International Journal of Computer Vision, 2014, 106(2):115-137.
[20] Hu Y, Song R, Li Y. Efficient coarse-to-fine patch match for large displacement optical flow[C]// Proceed-
ings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016:5704-5712.
[21] Lowe D G . Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.

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备注/Memo

收稿日期: 2017-05-17; 修回日期: 2017-07-24.
作者简介: 何凯(1972—), 男, 博士, 副教授.
通讯作者: 何凯, hekai@tju.edu.cn.
基金项目: 国家自然科学基金资助项目(61271326).
Supported by the National Natural Science Foundation of China(No.,61271326).

更新日期/Last Update: 2018-05-10