Line Matching Algorithm Based on Pair-wise Geometric Features and Individual Line Descriptor Constraints

LIU Suyan; WANG Jingxue; SHEN Zhaoyu; WANG Qiang

doi:10.13203/j.whugis20210147

Volume 48 Issue 6

Jun. 2023

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2023 > 48(6): 936-949. > DOI: 10.13203/j.whugis20210147

LIU Suyan, WANG Jingxue, SHEN Zhaoyu, WANG Qiang. Line Matching Algorithm Based on Pair-wise Geometric Features and Individual Line Descriptor Constraints[J]. Geomatics and Information Science of Wuhan University, 2023, 48(6): 936-949. DOI: 10.13203/j.whugis20210147

Citation:

PDF (31252 KB)

Line Matching Algorithm Based on Pair-wise Geometric Features and Individual Line Descriptor Constraints

1.
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2.
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
3.
School of Geography and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China

More Information

Received Date: March 23, 2022
Available Online: June 11, 2023
Published Date: June 04, 2023

Graphical Abstract

Abstract

Abstract

Objectives In the field of line matching and checking, the geometric attributes of the individual line are weakly stable, and the non one-to-one matching results are difficult to be checked. To address the problems above, a line matching algorithm based on pair-wise geometric features and individual line descriptor constraints was proposed.
Methods For matching process, first, two line segments which satisfied certain geometric constraints in the neighborhood were grouped into a line pair and matched as a whole. Second, the epipolar constraint of the intersection in the line pair was used to determine the matching range. And characteristic angles within line pairs, distance ratio between line segments and the radiation information of the neighborhood of line pairs were used to narrow the range of the matching candidates. Finally, the final matching pairs was obtained by calculating the gray similarity of triangle region. For checking process, initially according to the angle of line with epipolar and the slope of line, the corresponding relation between individual lines was established. Moreover, each corresponding line pair was split into two groups of corresponding individual lines. Then, descriptors were established for two single lines in each group and the similarity between the two line descriptors was calculated. Eventually, collinear geometry and descriptor similarity were combined to check the matching results, eliminate the false matches. Collinear lines in the results were merged and one-to-one matching results were obtained.
Results Aerial images with typical texture features and close-range images of different transformation types were selected for experiments. The results demonstrate that the proposed algorithm has a high matching accuracy. The matching accuracy rate is higher than 95% in complex scenes with similar texture, perspective change, rotation change, scale change and illumination change.
Conclusions The proposed algorithm has good robustness to different types of images, and also has advantages for complex line matching check.
- line matching,
- pair-wise line matching,
- results checking,
- collinear geometry,
- line descriptor

FullText(HTML)

References (27)

References

[1]	Hofer M, Maurer M, Bischof H. Improving Sparse 3D Models for Man-Made Environments Using Line-Based 3D Reconstruction[C]//IEEE 2014 2nd International Conference on 3D Vision, Salt Lake, USA, 2014.
[2]	陆胜寒. 基于影像几何约束匹配策略的三维重建方法研究与实现[D]. 北京: 清华大学, 2018. Lu Shenghan. Study and Implementation of Image 3-D Reconstruction Based on Feature Matching Strategy by Geometric Constraints[D]. Beijing: Tsinghua University, 2018.
[3]	王玮琦, 游雄, 杨剑, 等. 一种改进匹配点对选取策略的Elastic Fusion室内三维重建算法[J]. 武汉大学学报(信息科学版), 2020, 45(9): 1469-1477. doi: 10.13203/j.whugis20180278 Wang Weiqi, You Xiong, Yang Jian, et al. Elastic Fusion for Indoor 3D Reconstruction with an Improved Matching Points Selection Strategy[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1469-1477. doi: 10.13203/j.whugis20180278
[4]	曹林. 基于倾斜影像线特征的建筑物三维模型重建与优化方法研究[D]. 郑州: 信息工程大学, 2020. Cao Lin. Research on 3D Building Model Reconstruction and Optimization Method Based on Line Festures of Oblique Images[D]. Zhengzhou: Information Engineering University, 2020.
[5]	池雨灿. 基于图像边缘特征的空间目标重建与评估方法[D]. 哈尔滨: 哈尔滨工业大学, 2019. Chi Yucan. Reconstruction and Evaluation Based on Image Edge Feature for Space Target[D]. Harbin: Harbin Institute of Technology, 2019.
[6]	梁艳. 基于近景图像序列的建筑物三维模型重建研究[D]. 南京: 南京师范大学, 2013. Liang Yan. Research on 3D Model Reconstruction of Building from Close Range Image Sequence[D]. Nanjing: Nanjing Normal University, 2013.
[7]	张焱鑫. 基于直线特征的多视图几何三维重建算法研究与实现[D]. 秦皇岛: 燕山大学, 2019. Zhang Yanxin. Research and Implementation of Multi-view Geometric 3D Reconstruction Algorithms Based on Linear Features[D]. Qinhuangdao: Yanshan University, 2019.
[8]	Hofer M, Maurer M, Bischof H. Efficient 3D Scene Abstraction Using Line Segments[J]. Computer Vision and Image Understanding, 2017, 157: 167-178. doi: 10.1016/j.cviu.2016.03.017
[9]	Schmid C, Zisserman A. Automatic Line Matching Across Views[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, USA, 2002.
[10]	梁艳, 盛业华, 张卡, 等. 利用局部仿射不变及核线约束的近景影像直线特征匹配[J]. 武汉大学学报(信息科学版), 2014, 39(2): 229-233. doi: 10.13203/j.whugis20120611 Liang Yan, Sheng Yehua, Zhang Ka, et al. Linear Feature Matching Method Based on Local Affine Invariant and Epipolar Constraint for Close-range Images[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 229-233. doi: 10.13203/j.whugis20120611
[11]	Schmid C, Zisserman A. The Geometry and Matching of Lines and Curves over Multiple Views[J]. International Journal of Computer Vision, 2000, 40(3): 199-233. doi: 10.1023/A:1008135310502
[12]	李畅, 刘亚文, 胡敏, 等. 面向街景立面三维重建的近景影像直线匹配方法研究[J]. 武汉大学学报(信息科学版), 2010, 35(12): 1461-1465. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201012019.htm Li Chang, Liu Yawen, Hu Min, et al. Study on Street Elevation 3D Reconstruction-oriented Straight Line Matching for Close-range Images[J]. Geomatics and Information Science of Wuhan University, 2010, 35(12): 1461-1465. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201012019.htm
[13]	Jia Q, Fan X, Gao X K, et al. Line Matching Based on Line-points Invariant and Local Homography[J]. Pattern Recognition, 2018, 81: 471-483. doi: 10.1016/j.patcog.2018.03.031
[14]	张云生, 朱庆, 吴波, 等. 一种基于三角网约束的立体影像线特征多级匹配方法[J]. 武汉大学学报(信息科学版), 2013, 38(5): 522-527. http://ch.whu.edu.cn/article/id/2634 Zhang Yunsheng, Zhu Qing, Wu Bo, et al. A Hierarchical Stereo Line Matching Method Based on a Triangle Constraint[J]. Geomatics and Information Science of Wuhan University, 2013, 38(5): 522-527. http://ch.whu.edu.cn/article/id/2634
[15]	Wang Z H, Wu F C, Hu Z Y. MSLD: A Robust Descriptor for Line Matching[J]. Pattern Recognition, 2009, 42(5): 941-953. doi: 10.1016/j.patcog.2008.08.035
[16]	Xing J, Wei Z Z, Zhang G J. A Line Matching Method Based on Multiple Intensity Ordering with Uniformly Spaced Sampling[J]. Sensors (Basel, Switzerland), 2020, 20(6): 1639. doi: 10.3390/s20061639
[17]	Wang L, Neumann U, You S Y. Wide-baseline Image Matching Using Line Signatures[C]//IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 2010.
[18]	Al-Shahri M, Yilmaz A. Line Matching in Wide-baseline Stereo: A Top-down Approach[J]. IEEE Transactions on Image Processing, 2014, 23(9): 4199-4210.
[19]	Li K, Yao J, Lu X, et al. Hierarchical Line Matching Based on Line-junction-line Structure Descriptor and Local Homography Estimation[J]. Neurocomputing, 2016, 184: 207-220. doi: 10.1016/j.neucom.2015.07.137
[20]	Li K, Yao J. Line Segment Matching and Reconstruction via Exploiting Coplanar Cues[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125: 33-49. doi: 10.1016/j.isprsjprs.2017.01.006
[21]	Kim H, Lee S. A Novel Line Matching Method Based on Intersection Context[C]//IEEE International Conference on Robotics and Automation, Anchorage, USA, 2010.
[22]	Kim H W, Lee S H. Simultaneous Line Matching and Epipolar Geometry Estimation Based on the Intersection Context of Coplanar Line Pairs[J]. Pattern Recognition Letters, 2012, 33(10): 1349-1363. doi: 10.1016/j.patrec.2012.03.014
[23]	Li K, Yao J, Lu X H. Robust Line Matching Based on Ray-point-ray Structure Descriptor[M]//Computer Vision: ACCV 2014 Workshops. Cham: Springer, 2015: 554-569.
[24]	OK A O, Wegner J D, Heipke C, et al. Matching of Straight Line Segments from Aerial Stereo Images of Urban Areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 74: 133-152. doi: 10.1016/j.isprsjprs.2012.09.003
[25]	王竞雪, 宋伟东, 王伟玺. 同名点及高程平面约束的航空影像直线匹配算法[J]. 测绘学报, 2016, 45(1): 87-95. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201601014.htm Wang Jingxue, Song Weidong, Wang Weixi. Line Matching Algorithm for Aerial Image Based on Corresponding Points and Zplane Constraints[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(1): 87-95. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB201601014.htm
[26]	王竞雪, 朱庆, 王伟玺. 顾及拓扑关系的立体影像直线特征可靠匹配算法[J]. 测绘学报, 2017, 46(11): 1850-1858. doi: 10.11947/j.AGCS.2017.20170162 Wang Jingxue, Zhu Qing, Wang Weixi. Reliable Line Matching Algorithm for Stereo Images with Topological Relationship[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(11): 1850-1858. doi: 10.11947/j.AGCS.2017.20170162
[27]	Tola E, Lepetit V, Fua P. Daisy: An Efficient Dense Descriptor Applied to Wide-baseline Stereo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 815-830.

[1]	ZHANG Fan, CHAI Hongzhou, WANG Min, XIAO Guorui, ZHANG Qiankun, DU Zhenqiang. Undifferenced and Uncombined PPP Ambiguity Resolution Combined with GPS/GLONASS Triple-Frequency Observations[J]. Geomatics and Information Science of Wuhan University, 2024, 49(10): 1900-1910. DOI: 10.13203/j.whugis20220315
[2]	SONG Weiwei, SONG Qisheng, HE Qianqian, GONG Xiaopeng, GU Shengfeng. Analysis of PPP-B2b Positioning Performance Enhanced by High-Precision Ionospheric Products[J]. Geomatics and Information Science of Wuhan University, 2024, 49(9): 1517-1526. DOI: 10.13203/j.whugis20230030
[3]	SONG Weiwei, ZHAO Xinke, LOU Yidong, SUN Weibin, ZHAO Zhengyu. Performance Evaluation of BDS-3 PPP-B2b Service[J]. Geomatics and Information Science of Wuhan University, 2023, 48(3): 408-415. DOI: 10.13203/j.whugis20200686
[4]	YAN Zhongbao, ZHANG Xiaohong. Partial Ambiguity Resolution Method and Results Analysis for GNSS Uncombined PPP[J]. Geomatics and Information Science of Wuhan University, 2022, 47(6): 979-989. DOI: 10.13203/j.whugis20220025
[5]	GU Shengfeng, DAI Chunqi, HE Chengpeng, FANG Lizhe, WANG Zihao. Analysis of Semi-tightly Coupled Multi-GNSS PPP-RTK/VIO for Vehicle Navigation in Urban Areas[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1852-1861. DOI: 10.13203/j.whugis20210615
[6]	YAO Yibin, FENG Xinying, PENG Wenjie, LIU Lei. Local Atmosphere Augmentation Based on CORS for Real-Time PPP[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12): 1739-1748. DOI: 10.13203/j.whugis20180131
[7]	SONG Chao, HAO Jinming. Instantaneous Re-convergence of Kinematic PPP by the Use of Relationship Between Multiple Receiver Ambiguity[J]. Geomatics and Information Science of Wuhan University, 2017, 42(5): 595-599, 690. DOI: 10.13203/j.whugis20140747
[8]	GUO Fei, ZHANG Xiaohong. Processing Capacity for GPS Data with Clock Slip Using Online PPP Services[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11): 1333-1336.
[9]	TU Rui, HUANG Guanwen, ZHANG Qin, WANG Li. The Research of Dual Frequency Solution Method for Single Frequency Precise Point Positioning(PPP) Based on SEID Model[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1187-1190.
[10]	ZHAO Jianhu, WANG Shengping, ZHANG Hongmei, WEN Weidong. Long-Distance and On-the-Fly GPS Tidal Level Measurement Based on GPS PPK/PPP[J]. Geomatics and Information Science of Wuhan University, 2008, 33(9): 910-913.

Cited By

Cited by

Periodical cited type(13)

1.	冯晓亮，陈欢，李厚芝. 不同观测环境中的多模GNSS数据质量自动化检测方法. 测绘工程. 2024(06): 56-61 .
2.	刘嘉伟，孙保琪，韩蕊，张喆，王侃，袁海波，杨旭海. GNSS多系统RTK授时性能分析. 导航定位与授时. 2023(03): 49-58 .
3.	王浩浩，郝明，庄文泉. GNSS实时卫星钟差估计在地震监测中的应用. 导航定位与授时. 2023(03): 108-116 .
4.	周长江，余海锋，王林伟，雷云平，岳彩亚. 无频间钟偏差改正的BDS-2三频非组合PPP随机模型优化. 测绘通报. 2023(12): 164-168 .
5.	潘丽静，刘翔，夏川茹，王雷雷. GNSS精密卫星钟差实时估计与分析. 城市勘测. 2021(06): 73-76 .
6.	郭磊，王甫红，桑吉章，张万威. 一种新的利用历元间位置变化量约束的GNSS导航算法. 武汉大学学报(信息科学版). 2020(01): 21-27 .
7.	陶钧，张柔. GPS/BeiDou/Galileo/GLONASS实时精密卫星钟差估计. 测绘地理信息. 2020(03): 102-106 .
8.	黄观文，王浩浩，谢威，曹钰. GNSS实时卫星钟差估计技术进展. 导航定位与授时. 2020(05): 1-9 .
9.	张浩，赵兴旺，陈佩文，谢毅. GPS/BDS卫星钟差融合解算模型及精度分析. 合肥工业大学学报(自然科学版). 2020(09): 1192-1196 .
10.	叶珍，李浩军. GNSS卫星钟差估计与结果分析. 导航定位与授时. 2019(03): 88-94 .
11.	盛剑锋，张彩红，谭凯. 一种全球导航卫星系统钟差估计优化方案的量化研究. 科学技术与工程. 2019(14): 14-21 .
12.	王尔申，赵珩，曲萍萍，庞涛，孙军. 基于拉格朗日插值法的卫星导航空间信号精度评估算法. 沈阳航空航天大学学报. 2019(04): 43-48 .
13.	李云，崔文刚. 精密单点定位技术发展及应用. 科学技术与工程. 2019(27): 1-11 .

Other cited types(10)

Get Citation

PDF

XML

Article views PDF downloads Cited by(23)

Line Matching Algorithm Based on Pair-wise Geometric Features and Individual Line Descriptor Constraints

Abstract

References

Related Articles

Cited by

Periodical cited type(13)

Other cited types(10)

Catalog

Related

Line Matching Algorithm Based on Pair-wise Geometric Features and Individual Line Descriptor Constraints

Abstract

References

Related Articles

Cited by

Periodical cited type(13)

Other cited types(10)

Catalog

Related

Export File

Citation

Format

Content