基于R-MI-rényi测度的可见光与红外图像配准
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摘要
针对红外与可见光图像配准的准确性与鲁棒性问题,提出一种基于R-MI-rényi测度的由粗到精红外与可见光图像配准方法。首先通过分析红外与可见光传感器的成像原理,确定红外与可见光传感器在相对位置固定时的配准变换参数;然后采用Hough变换检测模板图像的直线特征,并利用待配准图像对应直线段的长度和斜率确定粗配准参数;根据粗配准参数确定搜索区域与匹配窗口尺寸,利用rényi互信息与Harris角点函数相结合的R-MI-rényi匹配测度在粗配准对应区域内搜索匹配点对;最后使用RANSAC方法完成图像的精确配准并求解图像转换参数矩阵。分别选取标准测试图像集和真实测试图像集对本文方法和现有代表方法进行综合对比,实验结果表明,方法在像素误差、标准差以及时间消耗等方面均优于其他对比方法,说明方法具有较高的配准精度和效率、较好的鲁棒性,综合性能最优。
Aiming at the accuracy and robustness of infrared and visible images registration,a coarse-to-fine image registration method for infrared and visible images based on R-MI-rényi measurement is proposed in this paper. Firstly,the transformation parameters are determined when the infrared and visible sensors located at the fixed locations by analyzing the different principles of infrared and visible imaging. Secondly,the line features of different images are detected through the Hough transformation,and the parameters of coarse registration are computed with the length and slope of the detected straight-line segments. Thirdly,the sizes of searching regions and matching windows are defined with the parameters of coarse registration,and the matching couples of feature points in the corresponding areas are presented by using the R-MI-rényi measurement which combined the rényi mutual information and Harris function. Finally,the fine images registration is finished and the transformation matrix could be solved by RANSAC. The standard and real test image sets are employed to evaluate the proposed method and the typical existing methods. And the experimental results show that the proposed method has the better performances of the pixel error,standard error and time consumption compared to the other methods. It indicates that the proposed method has higher accuracy,rapider computing efficiency and better robustness.
Aiming at the accuracy and robustness of infrared and visible images registration,a coarse-to-fine image registration method for infrared and visible images based on R-MI-rényi measurement is proposed in this paper. Firstly,the transformation parameters are determined when the infrared and visible sensors located at the fixed locations by analyzing the different principles of infrared and visible imaging. Secondly,the line features of different images are detected through the Hough transformation,and the parameters of coarse registration are computed with the length and slope of the detected straight-line segments. Thirdly,the sizes of searching regions and matching windows are defined with the parameters of coarse registration,and the matching couples of feature points in the corresponding areas are presented by using the R-MI-rényi measurement which combined the rényi mutual information and Harris function. Finally,the fine images registration is finished and the transformation matrix could be solved by RANSAC. The standard and real test image sets are employed to evaluate the proposed method and the typical existing methods. And the experimental results show that the proposed method has the better performances of the pixel error,standard error and time consumption compared to the other methods. It indicates that the proposed method has higher accuracy,rapider computing efficiency and better robustness.
引文
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[2]LIU Z,BLASCH E,XUE Z,et al.Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision:A comparative study[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(1):94-109.
[3]吴成中,王耀南,冯明涛,等.基于神经网络的医药微弱异物视觉检测机器人[J].电子测量与仪器学报,2015,29(12):1746-1756.WU CH ZH,WANG Y N,FENG M T,et al.Neural network based pharmaceutical insoluble foreign matter inspection robot[J].Journal of Electronic Measurement and Istrumentation,2015,29(12):1746-1756.
[4]祝继华,周颐,王晓春,等.基于图像配准的栅格地图拼接方法[J].自动化学报,2015,41(2):285-294.ZHU J H,ZHOU Y,WANG X CH,et al.Grid map merging approach based on image registration[J].Acta Automatica Sinica,2015,41(2):285-294.
[5]ZITOVB,FLUSSER J.Image registration methods:A survey[J].Image&Vision Computing,2003,21(11):977-1000.
[6]ZHANG X,MEN T,LIU C,et al.Infrared and visible images registration using BEMD and MI[C].IEEE International Conference on Computer Science and Information Technology,2010:644-647.
[7]SUN M,ZHANG B,LIU J,et al.The registration of aerial infrared and visible images[C].IEEE International Conference on Educational and Information Technology,2010:438-442.
[8]ZHUANG Y,GAO K,MIU X.IR and visual image registration based on mutual information and PSO-Powell algorithm[C].International Symposium on Optoelectronic Technology and Application,2014:1-6.
[9]REDDY B S,CHATTERJI B N.An FFT-based technique for translation,rotation,and scale-invariant image registration[J].IEEE Transactions on Image Processing,1996,5(8):1266-1271.
[10]王彩玲,程勇,赵春霞,等.局部相位相关用于图像亚像素级配准技术研究[J].中国图象图形学报,2011,16(3):427-432.WANG C L,CHENG Y,ZHAO CH X,et al.Robust subpixel image registration technique based on local phase correlation[J].Journal of Image and Graphics,2011,16(3):427-432.
[11]GONG M,ZHAO S,JIAO L,et al.A novel coarse-tofine scheme for automatic image registration based on SIFT and mutual information[J].IEEE Transactions on Geoscience&Remote Sensing,2014,52(7):4328-4338.
[12]罗天健,刘秉瀚.融合特征的快速SURF配准算法[J].中国图象图形学报,2015,20(1):95-103.LUO T J,LIU B H.Fast SURF key-points image registration algorithm by fusion features[J].Journal of Image and Graphics,2015,20(1):95-103.
[13]BILODEAU G A,TORABI A,MORIN F.Visible and infrared image registration using trajectories and composite foreground images[J].Image&Vision Computing,2011,29(1):41-50.
[14]GONALVES H,CORTE-REAL L,GONALVES J.Automatic image registration through image segmentation and SIFT[J].IEEE Transactions on Geoscience&Remote Sensing,2011,49(7):2589-2600.
[15]WANG P,QU Z G,WANG P,et al.A coarse-to-fine matching algorithm for FLIR and optical satellite image registration[J].IEEE Geoscience&Remote Sensing Letters,2012,9(4):599-603.
[16]HAN J,PAUWELS E J,ZEEUW P D.Visible and infrared image registration in man-made environments employing hybrid visual features[J].Pattern Recognition Letters,2013,34(1):42-51.
[17]CHAKRAVORTY T,BILODEAU G A,GRANGER E.Automatic image registration in infrared-visible videos using polygon vertices[R].Technical Report,ar Xiv,2014.
[18]TIAN T,MEI X,YU Y,et al.Automatic visible and infrared face registration based on silhouette matching and robust transformation estimation[J].Infrared Physics&Technology,2015,69(3):145-154.
[19]许佳佳,张叶,张赫.基于改进Harris-SIFT算子的快速图像配准算法[J].电子测量与仪器学报,2015,29(1):48-54.XU J J,ZHANG Y,ZHANG H.Fast image registration algorithm based on improved Harris-SIFT descriptor[J].Journal of Electronic Measurement and Istrumentation,2015,29(1):48-54.