基于良分布的亚像素定位角点的图像配准

葛永新; 杨丹; 雷明

doi:10.3724/SP.J.1146.2008.00983

基于良分布的亚像素定位角点的图像配准

doi: 10.3724/SP.J.1146.2008.00983

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出版历程
- 收稿日期: 2008-08-04
- 修回日期: 2009-11-23
- 刊出日期: 2010-02-19

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

摘要

摘要: 针对Harris检测出的角点位置会发生偏移和易产生伪角点，以及在角点匹配过程中计算复杂，容易产生误匹配等缺点，该文提出了基于良分布的亚像素定位角点的图像配准方法。该方法首先使用多尺度Harris算子检测图像的角点作为初始兴趣点，并采用自适应非极大值抑制对兴趣点的数量进行限制，以减少后续过程的计算复杂度，提高算法效率，同时使得角点在图像中处于良分布状态。然后利用亚像素定位技术进行精确定位，排除伪角点和不稳定的角点。最后使用随机抽样一致性算法对初始匹配进行鲁棒的模型参数估计。实验结果表明算法配准效率改进明显，并具有良好的精确性和鲁棒性。
- 图像配准; 亚像素定位; 良分布; 随机抽样一致性
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.

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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

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