Efficient detection and recognition algorithm of reference points in photogrammetry

被引:6
作者
Li, Weimin [1 ]
Liu, Gang [1 ]
Zhu, Lichun [2 ]
Li, Xiaofeng [1 ]
Zhang, Yuhai [1 ]
Shan, Siyu [1 ]
机构
[1] Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, 96 JinZhai Rd,POB 230027, Hefei, Anhui, Peoples R China
[2] Natl Astron Observ, Beijing 100012, Peoples R China
来源
OPTICS, PHOTONICS AND DIGITAL TECHNOLOGIES FOR IMAGING APPLICATIONS IV | 2016年 / 9896卷
关键词
photogrammetry; CCT(circular coded target); sub-pixel;
D O I
10.1117/12.2225416
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In photogrammetry, an approach of automatic detection and recognition on reference points have been proposed to meet the requirements on detection and matching of reference points. The reference points used here are the CCT(circular coded target), which compose of two parts: the round target point in central region and the circular encoding band in surrounding region. Firstly, the contours of image are extracted, after that noises and disturbances of the image are filtered out by means of a series of criteria, such as the area of the contours, the correlation coefficient between two regions of contours etc. Secondly, the cubic spline interpolation is adopted to process the central contour region of the CCT. The contours of the interpolated image are extracted again, then the least square ellipse fitting is performed to calculate the center coordinates of the CCT. Finally, the encoded value is obtained by the angle information from the circular encoding band of the CCT. From the experiment results, the location precision of the CCT can be achieved to sub-pixel level of the algorithm presented. Meanwhile the recognition accuracy is pretty high, even if the background of the image is complex and full of disturbances. In addition, the property of the algorithm is robust. Furthermore, the runtime of the algorithm is fast.
引用
收藏
页数:10
相关论文
共 50 条
[21]   Evaluating likelihood-based photogrammetry for individual recognition of four species of northern ungulates [J].
Isobel F. G. Ness ;
Thomas S. Jung ;
Fiona K. A. Schmiegelow .
Mammalian Biology, 2022, 102 :701-718
[22]   A new camera calibration method based on orientation points in digital close-range industrial photogrammetry [J].
Xuemei, Huang ;
Mingjian, Zhu .
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, 8 (06) :301-310
[23]   Rockfall detection along road networks using close range photogrammetry [J].
Partsinevelos, Panagiotis ;
Mertikas, Stelios ;
Agioutantis, Zacharias ;
Tsioukas, Vassilis ;
Tripolitsiotis, Achilleas ;
Zervos, Panagiotis .
SECOND INTERNATIONAL CONFERENCE ON REMOTE SENSING AND GEOINFORMATION OF THE ENVIRONMENT (RSCY2014), 2014, 9229
[24]   USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS [J].
Minarik, R. ;
Langhammer, J. .
XXIII ISPRS CONGRESS, COMMISSION VIII, 2016, 41 (B8) :711-718
[25]   A SYSTEMATIC REVIEW OF THE ANGULAR VALUES OBTAINED BY COMPUTERIZED PHOTOGRAMMETRY IN SAGITTAL PLANE: A PROPOSAL FOR REFERENCE VALUES [J].
Krawczky, Bruna ;
Pacheco, Antonio G. ;
Mainenti, Miriam R. M. .
JOURNAL OF MANIPULATIVE AND PHYSIOLOGICAL THERAPEUTICS, 2014, 37 (04) :269-275
[26]   Better virtual objects placement in real world through photogrammetry for object recognition and spatial anchoring [J].
Di Summa, M. ;
Griseta, M. ;
Patruno, C. ;
Vigano, G. P. ;
Reno, V ;
Mosca, N. .
OPTICAL ARCHITECTURES FOR DISPLAYS AND SENSING IN AUGMENTED, VIRTUAL, AND MIXED REALITY, AR, VR, MR IV, 2023, 12449
[27]   A novel capacity expansion and recognition acceleration method for dot-dispersing coded targets in photogrammetry [J].
Wang, Meng ;
Guo, Yuhan ;
Wang, Qiang ;
Liu, Yang ;
Liu, Jiaxin ;
Song, Xin ;
Wang, Guo ;
Zhang, Hu .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (12)
[28]   Prediction and Optimization Algorithm for Intersection Point of Spatial Multi-Lines Based on Photogrammetry [J].
Zhao, Chengli ;
Xiao, Hao ;
Zhao, Zhangyan ;
Wang, Guoxian .
SENSORS, 2022, 22 (24)
[29]   Photogrammetry-based structural damage detection by tracking a visible laser line [J].
Xu, Yongfeng .
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2020, 19 (01) :322-336
[30]   Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry [J].
Lalak, Marta ;
Wierzbicki, Damian ;
Kedzierski, Michal .
REMOTE SENSING, 2020, 12 (20) :1-35