近景摄影测量下的视场关系定量表达
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摘要
近景摄影测量下的视场关系指较小空间范围中不同视场之间存在的位姿关系,它用来确定不同摄像机所代表的参考坐标系之间的旋转量与平移量。它在交通监控、目标识别与定位、居家安全保障等方面有着广泛的应用。本文在近景摄影测量的框架下,提出了一种确定两种不同视场之间位姿的方法,具体步骤为:①在大视场下进行变化检测,当有变化区域被检测到时,获得原始图像,同时在小视场下获得样本图像集合;②对原始图像和样本图像集合进行相对定向,得到样本图像相对于原始图像的空间姿态参数;③将原始图像和样本图像进行图像匹配,获取匹配度最高的样本图像,该样本图像存储的姿态参数即是拍摄瞬间小视场相对于大视场的位姿;④按照步骤③中获取的姿态参数调整小视场下的摄像机空间位姿,使其对准变化区域。试验表明该方法能够有效支持空间范围较小区域内视场关系的定量描述。
The relationship at the field of view under close range photogrammetry refers to position and pose relationship at different fields of view in a small space range,which is used to determine the rotation and translation of the reference coordinates,represented by different cameras.Under the framework of close range photogrammetry,a method for determining the position and pose between two different fields of view is proposed in this paper,of which the concrete steps are:①Change detection from the large field of view,when the change area is detected,the original image is obtained at the same time,and the sample images set were obtained under the small field of view.②The original photo and the sample photos are relatively orientated,and the spatial attitude parameters of the sample photos relative to the original photo are obtained.③The original image is matched with the sample images to obtain the sample image with the highest matching degree,the attitude parameters of the sample image are the spatial position and pose of the samll field of view relative to the large field of view.④According to the attitude parameters obtained in step 3,the camera position and pose is adjusted under the small field of view so that it can be aligned to the changing area.Experiments show that this method can effectively support the quantitative description of the field of view in a small range.
The relationship at the field of view under close range photogrammetry refers to position and pose relationship at different fields of view in a small space range,which is used to determine the rotation and translation of the reference coordinates,represented by different cameras.Under the framework of close range photogrammetry,a method for determining the position and pose between two different fields of view is proposed in this paper,of which the concrete steps are:①Change detection from the large field of view,when the change area is detected,the original image is obtained at the same time,and the sample images set were obtained under the small field of view.②The original photo and the sample photos are relatively orientated,and the spatial attitude parameters of the sample photos relative to the original photo are obtained.③The original image is matched with the sample images to obtain the sample image with the highest matching degree,the attitude parameters of the sample image are the spatial position and pose of the samll field of view relative to the large field of view.④According to the attitude parameters obtained in step 3,the camera position and pose is adjusted under the small field of view so that it can be aligned to the changing area.Experiments show that this method can effectively support the quantitative description of the field of view in a small range.
引文
[1]GILLIES D.Close range photogrammetry[J].Photogrammetric Record,2015,30(151):318-322.
[2]徐嵩,孙秀霞,刘希,等.基于矩形的摄像机自标定几何方法[J].光学学报,2014,34(11):225-238.
[3]朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010,30(5):1297-1303.
[4]鲁亚楠,万子敬,王向军.一种无公共视场相机位置关系的求解方法[J].应用光学,2017,38(3):400-405.
[5]杨景豪,刘巍,刘阳,等.双目立体视觉测量系统的标定[J].光学精密工程,2016,24(2):300-308.
[6]霍炬,杨卫,杨明.基于消隐点几何特性的摄像机自标定方法[J].光学学报,2010,30(2):465-472.
[7]郑顺义,黄荣永,郭宝云,等.附约束条件的立体相机标定方法[J].测绘学报,2012,41(6):877-885.
[8]SU J.Camera calibration based on receptive fields[J].Pattern Recognition,2007,40(10):2837-2845.
[9]张超,韩成,杨华民,等.基于位姿约束的大视场双目视觉标定算法[J].光学学报,2016,36(1):197-206.
[10]ZHANG Z.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2000,22(11):1330-1334.
[11]蒯杨柳,文贡坚,回丙伟,等.利用多个小棋盘的大视场相机标定方法[J].测绘通报,2016(7):39-43.
[12]谢东海,钟若飞,吴俣,等.球面全景影像相对定向与精度验证[J].测绘学报,2017,46(11):1822-1829.
[13]BARNICH O,DROOGENBROECK M V.Vi Be:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[14]BROWN L G.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):325-376.
[15]LEORDEANU M,HEBERT M.A spectral technique for correspondence problems using pairwise constraints[C]∥Tenth IEEE International Conference on Computer Vision.[S.l.]:IEEE,2005.
[16]MA J,CHAN C W,CANTERS F.Fully automatic subpixel image registration of multiangle CHRIS/Proba data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(7):2829-2839.
[17]ZHANG D R,LE Y U,CAI Z G.Matching method based on Harris operator and wavelet pyramid[J].Journal of Zhejiang University,2007,34(4):465-468.
[18]SHI W,SHAKER A.The line-based transformation model(LBTM)for image-to-image registration of highresolution satellite image data[J].International Journal of Remote Sensing,2006,27(14):3001-3012.
[19]GONCALVES H,GONCALVES J A,CORTE-REAL L.HAIRIS:a method for automatic image registration through histogram-based image segmentation[J].IEEETransactions on Image Processing,2011,20(3):776-89.
[20]BAY H,ESS A,TUYTELAARS T,et al.Speeded-up robust features[J].Computer Vision&Image Understanding,2008,110(3):404-417.
[21]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[2]徐嵩,孙秀霞,刘希,等.基于矩形的摄像机自标定几何方法[J].光学学报,2014,34(11):225-238.
[3]朱嘉,李醒飞,徐颖欣.摄像机的一种主动视觉标定方法[J].光学学报,2010,30(5):1297-1303.
[4]鲁亚楠,万子敬,王向军.一种无公共视场相机位置关系的求解方法[J].应用光学,2017,38(3):400-405.
[5]杨景豪,刘巍,刘阳,等.双目立体视觉测量系统的标定[J].光学精密工程,2016,24(2):300-308.
[6]霍炬,杨卫,杨明.基于消隐点几何特性的摄像机自标定方法[J].光学学报,2010,30(2):465-472.
[7]郑顺义,黄荣永,郭宝云,等.附约束条件的立体相机标定方法[J].测绘学报,2012,41(6):877-885.
[8]SU J.Camera calibration based on receptive fields[J].Pattern Recognition,2007,40(10):2837-2845.
[9]张超,韩成,杨华民,等.基于位姿约束的大视场双目视觉标定算法[J].光学学报,2016,36(1):197-206.
[10]ZHANG Z.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2000,22(11):1330-1334.
[11]蒯杨柳,文贡坚,回丙伟,等.利用多个小棋盘的大视场相机标定方法[J].测绘通报,2016(7):39-43.
[12]谢东海,钟若飞,吴俣,等.球面全景影像相对定向与精度验证[J].测绘学报,2017,46(11):1822-1829.
[13]BARNICH O,DROOGENBROECK M V.Vi Be:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[14]BROWN L G.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):325-376.
[15]LEORDEANU M,HEBERT M.A spectral technique for correspondence problems using pairwise constraints[C]∥Tenth IEEE International Conference on Computer Vision.[S.l.]:IEEE,2005.
[16]MA J,CHAN C W,CANTERS F.Fully automatic subpixel image registration of multiangle CHRIS/Proba data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(7):2829-2839.
[17]ZHANG D R,LE Y U,CAI Z G.Matching method based on Harris operator and wavelet pyramid[J].Journal of Zhejiang University,2007,34(4):465-468.
[18]SHI W,SHAKER A.The line-based transformation model(LBTM)for image-to-image registration of highresolution satellite image data[J].International Journal of Remote Sensing,2006,27(14):3001-3012.
[19]GONCALVES H,GONCALVES J A,CORTE-REAL L.HAIRIS:a method for automatic image registration through histogram-based image segmentation[J].IEEETransactions on Image Processing,2011,20(3):776-89.
[20]BAY H,ESS A,TUYTELAARS T,et al.Speeded-up robust features[J].Computer Vision&Image Understanding,2008,110(3):404-417.
[21]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.