|本期目录/Table of Contents|

[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]
点击复制

基于改进光流场模型的非刚性图像配准()
分享到:

《天津大学学报(自然科学版)》[ISSN:0493-2137/CN:12-1127/N]

卷:
期数:
2018年05
页码:
491-496
栏目:
论文
出版日期:
2018-05-15

文章信息/Info

Title:
Non-Rigid Image Registration Using Improved Optical Flow Field Model
文章编号:
0493-2137(2018)05-0491-06
作者:
何凯 闫佳星 魏颖 王阳
天津大学电气自动化与信息工程学院,天津 300072
Author(s):
He Kai Yan Jiaxing Wei Ying Wang Yang
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
关键词:
图像配准 非刚性形变 光流场模型 特征向量 双边滤波
Keywords:
image registration non-rigid deformation optical flow field model character vector bilateral filtering
分类号:
TP391.41
DOI:
10.11784/tdxbz201705050
文献标志码:
A
摘要:
非刚性形变具有局部变形严重、非线性特征明显、各点转换模型不统一等特点, 其形变前后图像的自动配准一直是计算机视觉领域的研究热点和难点.为解决上述问题, 提出一种改进的光流场模型, 将特征向量守恒作为约束条件, 较好地解决了大位移形变配准问题, 同时保留了图像的细节信息, 提高了光流估计的准确性.此外, 为解决传统光流场模型中因过度平滑而导致的图像边缘模糊问题, 对金字塔各层位移场作了双边滤波, 以保证去除噪点的同时, 保留图像的边缘特征.实验结果表明, 该算法模型鲁棒性较高, 对于复杂形变的非刚性图像仍然能够获得理想的配准效果.
Abstract:
Deformation of non-rigid objects has the characters of obvious local transformation,serious non-linearity and lacking of uniform transformation model. Due to these reasons,non-rigid image automatic registration before and after deformation is always a hot topic and difficulty in the computer vision field. To solve this challenging problem,an improved optical flow model is proposed using the conservative character vector as the constraint condition. Thus,the problem of large displacement registration is solved,the image detail is well maintained,then the accuracy of the estimated optical flow is improved. Moreover,to overcome the edge blur problem caused by the over-smoothness of traditional optical flow model,bilateral filtering is utilized on the displacement field of each level of pyramid to remove noise while preserving the image edge features. Experimental results demonstrate that the proposed model is robust and suitable to deal with the non-rigid images with complex deformation to achieve ideal registration effect.

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.

相似文献/References:

[1]杨淑莹,任翠池,张成,等.基于机器视觉的齿轮产品外观缺陷检测[J].天津大学学报(自然科学版),2007,(09):1111.
[2]张素,苏和,陆雪松,等.基于形状匹配的快速图像配准[J].天津大学学报(自然科学版),2008,(04):433.
[3]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(自然科学版),2008,(12):1465.
[4]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(自然科学版),2006,(11):1375.

备注/Memo

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