Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
  • Indexed by
  • Korea Citation Index (KCI)

Journal Search

  1. Home
  2. All Issues
  3. 2024-06 (Vol.28 No.3)
  4. 10.11112/jksmi.2024.28.3.74
XML PDF INFO REF

References

1 
Spencer Jr, B. F., Hoskere, V., and Narazaki, Y. (2019), Advances in computer vision-based civil infrastructure inspection and monitoring, Engineering, 5(2), 199-222.DOI
2 
Celik, O., Dong, C. Z., and Catbas, F. N. (2018), A computer vision approach for the load time history estimation of lively individuals and crowds, Computers & Structures, 200, 32-52.DOI
3 
Kim, S. W., Jeon, B. G., Kim, N. S., and Park, J. C. (2013), Vision-based monitoring system for evaluating cable tensile forces on a cable-stayed bridge, Structural Health Monitoring, 12(5-6), 440-456.DOI
4 
Cha, Y. J., Chen, J. G., and Buyukozturk, O. (2017), Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters, Engineering Structures, 132, 300-313.DOI
5 
Feng, D., and Feng, M. Q. (2015), Model updating of railway bridge using in situ dynamic displacement measurement under trainloads, Journal of Bridge Engineering, 20(12), 04015019.DOI
6 
Poozesh, P., Sarrafi, A., Mao, Z., and Niezrecki, C. (2017), Modal parameter estimation from optically-measured data using a hybrid output-only system identification method, Measurement, 110, 134-145.DOI
7 
Lee, J. J., and Shinozuka, M. (2006), A vision-based system for remote sensing of bridge displacement, Ndt & E International, 39(5), 425-431.DOI
8 
Feng, D., Feng, M. Q., Ozer, E., and Fukuda, Y. (2015), A vision-based sensor for noncontact structural displacement measurement, Sensors, 15(7), 16557-16575.DOI
9 
Yoon, H., Elanwar, H., Choi, H., Golparvar‐Fard, M., and Spencer Jr, B. F. (2016), Target‐free approach for vision‐based structural system identification using consumer‐grade cameras, Structural Control and Health Monitoring, 23(12), 1405-1416.DOI
10 
Xu, Y., and Brownjohn, J. M. (2018). Review of machine-vision based methodologies for displacement measurement in civil structures. Journal of Civil Structural Health Monitoring, 8, 91-110.DOI
11 
Kohut, P., Holak, K., Uhl, T., Ortyl, Ł., Owerko, T., Kuras, P., and Kocierz, R. (2013), Monitoring of a civil structure’s state based on noncontact measurements, Structural Health Monitoring, 12(5-6), 411-429.DOI
12 
Luo, L., Feng, M. Q., and Wu, J. (2020), A comprehensive alleviation technique for optical‐turbulence‐induced errors in vision‐based displacement measurement, Structural Control and Health Monitoring, 27(3), e2496.DOI
13 
Fukuda, Y., Feng, M. Q., and Shinozuka, M. (2010), Cost‐effective vision‐based system for monitoring dynamic response of civil engineering structures, Structural Control and Health Monitoring, 17(8), 918-936.DOI
14 
Lucas, B. D., and Kanade, T. (1981, August), An iterative image registration technique with an application to stereo vision, Proceeding of 7th international joint conference on Artificial intelligence, Canadam Vol. 2, 674-679.URL
15 
Tomasi, C., and Kanade, T. (1991), Detection and tracking of point, Int J Comput Vis, 9, 137-154.URL
16 
Shi, J., and Tomasi, C. (1994, June), Good features to track, 1994 Proceedings of IEEE conference on computer vision and pattern recognition, IEEE, Seattle WA USA, 593-600.DOI
17 
Lowe, D. G. (2004), Distinctive image features from scale- invariant keypoints, International Journal of Computer Vision, 60, 91-110.DOI
18 
Bay, H., Tuytelaars, T., and Van Gool, L. (2006), Surf: Speeded up robust features, Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Springer Berlin Heidelberg, Graz, Austria, 404-417.DOI
19 
Alcantarilla, P. F., Bartoli, A., and Davison, A. J. (2012), KAZE features, Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Springer Berlin Heidelberg, 214-227.DOI
20 
Choi, Y., Farkoushi, M. G., Hong, S., and Shon, H. G. (2019), Feature-based Matching Algorithms for Registration between LiDAR Point Cloud Intensity Data Acquired from MMS and Image Data from UAV, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartograph, 37(6), 453-464 (in Korean).DOI
21 
Hong, S. and Shin, H. S. (2020), Comparative Performance Analysis of Feature Detection and Matching Methods for Lunar Terrain Images, Journal of the Korean Society of Civil Engineers, 40(4), 437-444 (in Korean).DOI
22 
Lee, T. H., Park, J. T., Lee, S. H., and Park, S. Z. (2022), Performance of Feature-based Stitching Algorithms for Multiple Images Captured by Tunnel Scanning System, Journal of the Korea Institute for Structural Maintenance and Inspection, 26(5), 30-42 (in Korean).DOI
23 
Harris, C., and Stephens, M. (1988, August), A combined corner and edge detector, In Alvey Vision Conference, 15(50), 147-151.URL
24 
Moravec, H. P. (1980), Obstacle avoidance and navigation in the real world by a seeing robot rover. Stanford University.URL
25 
Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011, November), BRISK: Binary robust invariant scalable keypoints, In 2011 International Conference on Computer Vision, IEEE, 2548-2555.DOI
26 
Rosten, E., and Drummond, T. (2006), Machine learning for high-speed corner detection. In Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz Austria, Springer Berlin Heidelberg, 430-443.DOI
27 
Torr, P. H., and Zisserman, A. (2000), MLESAC: A new robust estimator with application to estimating image geometry, Computer Vision and Image Understanding, 78(1), 138-156.DOI
28 
Badali, A. P., Zhang, Y., Carr, P., Thomas, P. J., and Hornsey, R. I. (2005, October), Scale factor in digital cameras, In Photonic Applications in Biosensing and Imaging, SPIE, 5969, 556-565.DOI
29 
Hijazi, A., Friedl, A., and Kahler, C. J. (2011), Influence of camera’s optical axis non-perpendicularity on measurement accuracy of two-dimensional digital image correlation, Jordan Journal of Mechanical and Industrial Engineering, 5(4), 1-10.URL