[1]袁丽英,刘佳,王飞越.基于SURF的图像配准改进算法[J].探测与控制学报,2020,42(02):65.[doi:.]
 YUAN Liying,LIU Jia,WANG Feiyue.An Improved Algorithm of SURF Image Registration[J].,2020,42(02):65.[doi:.]
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基于SURF的图像配准改进算法()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
42
期数:
2020年02
页码:
65
栏目:
出版日期:
2020-04-15

文章信息/Info

Title:
An Improved Algorithm of SURF Image Registration
文章编号:
1008-1194(2020)02-0065-06
作者:
袁丽英刘佳王飞越
哈尔滨理工大学自动化学院,黑龙江 哈尔滨 150080
Author(s):
YUAN Liying LIU Jia WANG Feiyue
Harbin University of Science and Technology, Harbin 150080, China
关键词:
图像配准SURF算法双边滤波肯德尔系数
Keywords:
image registration SURF algorithm bilateral filtering Kendall coefficient
分类号:
TP751
DOI:
.
文献标志码:
A
摘要:
针对SIFT图像配准算法存在配准精度低的问题,提出了基于SURF的图像配准改进算法。该算法在特征点提取之前,对图像进行双边滤波,减少错误来源,特征匹配初始阶段使用自适应阈值约束代替传统固定阈值,减少最近邻域与次近邻域之比对匹配结果的影响,加入肯德尔系数约束对匹配对提纯,提高配准精度,最后通过RANSAC算法和LSM迭代求解,进行结果处理。实验结果表明,改进的SURF算法在减少配准时间的基础上提高了正确匹配率。
Abstract:
Image registration refers to the alignment of two or more images of the same target in space. In view of the low registration accuracy of SURF image registration algorithm, we proposed an improved algorithm of image registration based on SURF. Firstly, before the extraction of feature points, bilateral filtering was performed to reduce the sources of errors, in the initial stage of feature matching, instead of traditional fixed thresholds, the adaptive threshold constraints became so widespread , which reduced the effect of matching results between nearest neighbor and second nearest neighbor, then the Kendall coefficient constraint pair was added to purify the matching pair to improve the registration accuracy, Finally, through RANSAC algorithm and LSM iteration to get the results processed. Experimental results showed that the improved SURF algorithm promoted the correct matching rate on the basis of reducing registration time.

参考文献/References:

[1]徐丽燕. 基于特征点的遥感图像配准方法及应用研究[D]. 南京:南京理工大学, 2012.
[2]周军太, 龙永红. 一种改进SURF算法的图像配准[J]. 湖南工业大学学报, 2011, 25(2):95-99.
[3]朱翙. 基于SURF算法的多光谱序列图像配准研究[D]. 西安:西安电子科技大学, 2013.
[4]Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision. 2004,60(2):91-110.
[5]Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points[C]// Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference. US:IEEE, 2002:525-531.
[6]徐丽燕, 王静, 邱军,等. 基于特征点的多光谱遥感图像配准[J]. 计算机科学, 2011, 38(7):280-282.
[7]王晓年, 邱立可, 程宇,等. 一种基于环间面积比的旋转、平移和缩放不变性描述符[J]. 模式识别与人工智能, 2012, 25(1):82-88.
[8]曾朝阳, 程相正, 陈杭,等. 基于改进SURF算子的高低分辨率图像配准方法[J]. 激光与红外, 2014(2):207-212.
[9]张凤晶, 王志强, 吴迪,等. 基于SURF的图像配准改进算法[J]. 长春理工大学学报(自然科学版), 2016(1):112-115.
[10]李天佐,刘丽萍,孙学宏,等.基于改进Harris-SURF算子 的遥感图像配准算法[J].信息通信,2017(11):9-10
[11]刘瑜, 刘缠牢, 苏海. 一种基于结构光双目视觉的特征匹配算法研究[J]. 光学仪器, 2014, 36(2):161-166.
[12]王永明, 王贵锦. 图像局部不变性特征与描述[M]. 北京:国防工业出版社, 2010.
[13]王露露, 张洪, 高忠国. 基于SURF的目标跟踪算法[J]. 江南大学学报(自然科学版), 2012, 11(5):515-518.
[14]亓辰. 高光谱与高空间分辨率遥感图像融合算法研究[D]. 哈尔滨:哈尔滨工业大学, 2008.
[15]卜珂. 基于SURF的图像配准与拼接技术研究[D]. 大连:大连理工大学, 2009.
[16]刘金硕, 曾秋梅, 邹斌,等. 快速鲁棒特征算法的CUDA加速优化[J]. 计算机科学, 2014, 41(4):24-27.
[17]Barbara Z, Jan F. Image registration methods: A survey[J]. Image and vision computing, 2003,21(11):977-1000.
[18]Martin A. Fischler,Robert C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM,1981,24(6):381-395.
[19]C.Tomasi, R.Manduchi. Bilateral filtering for gray and color images[C]//IEEE International Conference on Computer Vision. US:IEEE,1998,839-844.
[20]史露, 苏刚, 韩飞. 基于SIFT和SURF图像拼接算法的改进算法[J]. 计算机应用与软件, 2013, 30(6):72-74.bu

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备注/Memo

备注/Memo:
收稿日期:2019-11-11
基金项目:国家自然科学基金项目资助(61305001)
作者简介:袁丽英(1971—),女,博士,教授,硕士生导师,研究方向:智能控制及图像处理技术。E-mail:80381903qq.com。
更新日期/Last Update: 2020-05-15