Image Registration Based on Well-Distributed Corners with Sub-Pixel Localization

Ge Yong-xin; Yang Dan; Lei Ming

doi:10.3724/SP.J.1146.2008.00983

Volume 32 Issue 2

Aug. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(2): 427-432

Ge Yong-xin, Yang Dan, Lei Ming. Image Registration Based on Well-Distributed Corners with Sub-Pixel Localization[J]. Journal of Electronics & Information Technology, 2010, 32(2): 427-432. doi: 10.3724/SP.J.1146.2008.00983

Citation:

Ge Yong-xin, Yang Dan, Lei Ming. Image Registration Based on Well-Distributed Corners with Sub-Pixel Localization[J]. Journal of Electronics & Information Technology, 2010, 32(2): 427-432. doi: 10.3724/SP.J.1146.2008.00983

Citation:

PDF( 1460 KB)

Image Registration Based on Well-Distributed Corners with Sub-Pixel Localization

doi: 10.3724/SP.J.1146.2008.00983

Received Date: 2008-08-04
Rev Recd Date: 2009-11-23
Publish Date: 2010-02-19

Abstract

Abstract

To deal with the false and unstable corners, high computational complexity and incorrect matching, a new image registration algorithm is proposed based on corners which are well-distributed in image and with sub-pixel localization precision. Firstly corners in an image are detected by multi-scale Harris operator, which are taken as initial interest points. And then adaptive non-maximal suppression is used to limit the number of interest points, so the computational complexity is decreased and the efficiency of the algorithm is improved. At the mean time, the corners are made to be well-distributed in image. Since the location of initial Harris corners have offset and false corners have existed, sub-pixel localization technique is applied to determine the location of corners and eliminate the false and unstable corners in this process. Finally, RANSAC is used to estimate the parameters robustly based on initial matching. Experiments showed that the proposed algorithm has a good performance of efficiency, accuracy and robustness.

FullText(HTML)

References(1)

References

Barbara Zitova and Jan Flusser. Image registration methods: a survey[J].Image and Vision Computing.2003, 21(11):977-1000[2]刘贵喜，王雷. 基于区域选择和特征点匹配的图像配准算法[J]. 光电子激光，2007, 18(8): 999-1002.Liu Gui-xi and Wang Lei. An image registration method based on region selecting and feature points matching[J].Journal of OptoelectronicsLaser.2007, 18(8):999-1002[3]王婧，朱梦宇，赵保军，何佩琨. 基于小波和改进型Hausdorff距离的遥感图像配准方法[J]. 电子学报，2006, 34(12): 2167-2169.Wang Jing, Zhu Meng-yu, Zhao Bao-jun, and He Pei-kun. A remote sensing image registration method based on wavelet decomposition and the improved Hausdorff Distance[J]. Acta Electronica Sinca, 2006, 34(12): 2167-2169.[4]付朝霞，韩焱，咎波. 基于角点检测的图像镶嵌算法[J]. 光电工程，2007, 30(5): 126-130.Fu Zhao-xia, Han Yan, and Zan Bo. Method of image mosaic based on corner detection[J]. Opto-Electronic Engineering, 2007, 30(5): 126-130.[5]马丽涛, 杨丹, 张小洪, 李博. 一种新的基于条件数的图像配准算法[J]. 中国图象图形学报，2008, 13(2): 277-283.Ma Li-tao, Yang Dan, Zhang Xiao-hong, and Li Bo. A new method for image registration based on condition number[J]. Journal of Image and Graphics, 2008, 13(2): 277-283.[6]徐玮，王炜，张茂军，吴玲达. 一种基于角点匹配的视图合成方法[J]. 系统仿真学报，2007, 19(14): 3263-3265.Xu Wei, Wang Wei, Zhang Mao-jun, and Wu Ling-da. Corner matching-based approach of view synthesis[J]. Journal of System Simulation, 2007, 19(14): 3263-3265.[7]Harris C and Stephens M J. A combined corner and edge detector[C]. Proc. Fourth Alvey Vision Conference, Manchester, UK, 1988: 147-152.[8]Mikolajczyk K and Schmid C. An affine invariant interest point detector[C]. Proc. Seventh European Conf. Computer Vision, Copenhagen, Denmark, 2002: 128-142.Brown M and Lowe D G. Invariant features from interest point groups[C]. In British Machine Vision Conference, Cardiff, Wales, 2002: 656-665.[9]Brown M, Szeliski R, and Winder S. Multi-image matching using multi-scale oriented patches[C]. In Proceedings of the International Conference on Computer Vision and Pattern Recognition, San Diego, may, 2005: 510-517.[10]Fischler M A and Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J].Communications of ACM.1981, 24(6):381-395

Relative Articles

Supplements(0)

Cited By

Proportional views