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NTIS 바로가기ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), v.144, 2018년, pp.180 - 188
Attard, Leanne (Department of Communications and Computer Engineering, University of Malta) , Debono, Carl James (Department of Communications and Computer Engineering, University of Malta) , Valentino, Gianluca (Department of Communications and Computer Engineering, University of Malta) , Di Castro, Mario (Engineering Department, CERN)
Abstract During the last few decades many tunnelling projects were conducted in order to use limited land surface area more efficiently. Such underground constructions are used for transportation such as for railways, subways and roads, to host equipment used for experiments like particle accelerat...
Int. J. Adv. Res. Comput. Sci. Softw. Eng. Arya 05 05 299 2015 A review on image stitching and its different methods
10.1109/ISPA.2017.8073585 Attard, L., Debono, C.J., Valentino, G., Castro, M.D., 2017. Image mosaicing of tunnel wall images using high level features. In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, pp. 141-146. https://doi.org/10.1109/ISPA.2017.8073585.
Automat. Constr. Attard 91 142 2018 10.1016/j.autcon.2018.03.020 Vision-based change detection for inspection of tunnel liners
Asian J. Inform. Technol. Ayaho 5 553 2007 Automatic crack recognition system for concrete structures using image processing approach
Bauer, A., Gutjahr, K., Paar, G., Kontrus, H., Glatzl, R., 2015. Tunnel surface 3d reconstruction from unoriented image sequences. In: Proceedings of the 39th Annual Workshop of the Austrian Association for Pattern Recognition (OAGM). <https://arxiv.org/abs/1505.06237>.
Boving, K.G., 1989. Nde handbook. Butterworth-Heinemann, p. iii. https://doi.org/10.1016/B978-0-408-04392-2.50001-1. <http://www.sciencedirect.com/science/article/pii/B9780408043922500011>.
Castro, M.D., Masi, A., Lunghi, G., Losito, R., 2014. An incremental slam algorithm for indoor autonomous navigation.
Comput.-Aided Civ. Infrastruct. Eng. J. Cha 32 5 361 2017 10.1111/mice.12263 Deep learning-based crack damage detection using convolutional neural networks
J. Comput. Civ. Eng. Chaiyasarn 30 3 1 2013 Distortion-free image mosaicing for tunnel inspection based on robust cylindrical surface estimation
Inform. Technol. Geo-Eng. ChuanPeng 254 2010 Detection of tunnel water leakage based on image processing
Commercial UAV News. UAVs in Civil Infrastructure. <https://www.expouav.com/news/report/uavs-civil-infrastructure/> (online; accessed June 2018).
Crosilla 287 2003 Procrustes Analysis and Geodetic Sciences
Dorafshan, S., Maguire, M., Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations. Utah State University CEE Faculty Publications. https://doi.org/10.13140/RG.2.2.34024.47363.
Eastman 2008 BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors
10.1061/(ASCE)CF.1943-5509.0000094 Fahmy, M., Moselhi, O. Automated detection and location of leaks in water mains using infrared photography.(author abstract)(report), J. Perform. Construct. Facil. 24(3).
FLYABILITY . ELIOS, Inspect & Explore Indoor and Confined Spaces. <https://www.flyability.com/elios/> (online; accessed June 2018).
Forstner 2016 Photogrammetric Computer Vision - Statistics, Geometry, Orientation and Reconstruction
Frohlich, C., Mettenleiter, M. Terrestrial laser scanning-new perspectives in 3d surveying 36. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.215.8213&rep=rep1&type=pdf>.
Mach. Vis. Appl. Fujita 22 2 245 2011 10.1007/s00138-009-0244-5 A robust automatic crack detection method from noisy concrete surfaces
J. Vis. Commun. Image Rep. Ghosh 34 C 1 2016 10.1016/j.jvcir.2015.10.014 A survey on image mosaicing techniques
Photogrammetr. Rec. Granshaw 33 161 6 2018 10.1111/phor.12237 Structure from motion: origins and originality
South Afr. J. Photogram., Remote Sens. Cartogr. Gruen 14 175 1985 Adaptive least squares correlation: a powerful image matching technique
Adv. Eng. Inform. J. Huang 32 188 2017 10.1016/j.aei.2017.03.003 Inspection equipment study for subway tunnel defects by grey-scale image processing
Tunn. Undergr. Space Technol. Huang 77 166 2018 10.1016/j.tust.2018.04.002 Deep learning based image recognition for crack and leakage defects of metro shield tunnel
2014 Computer Vision: A Reference Guide, Springer Reference
10.1109/IECON.2002.1185314 Ito, A., Aoki, Y., Hashimoto, S., 2002. Accurate extraction and measurement of fine cracks from concrete block surface image. In: Proceedings of the IEEE 28th Annual Conference of the Industrial Electronics Society. IECON 02, vol. 3, pp. 2202-2207. https://doi.org/10.1109/IECON.2002.1185314.
Automat. Constr. Jahanshahi 22 Suppl. C 567 2012 10.1016/j.autcon.2011.11.018 Adaptive vision-based crack detection using 3d scene reconstruction for condition assessment of structures
10.1109/EESMS.2017.8052679 Jenkins, M.D., Buggy, T., Morison, G., 2017. An imaging system for visual inspection and structural condition monitoring of railway tunnels. In: Proceedings of the IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), pp. 1-6. https://doi.org/10.1109/EESMS.2017.8052679.
10.5194/isprsarchives-XXXIX-B5-223-2012 Jian, L., Youchuan, W., Xianjun, G., 2012. A new approach for subway tunnel deformation monitoring high-resolution terrestrial laser scanning. In: Proceedings of the XXII ISPRS Congress, vol. XXXIX, pp. 223-228.
10.5194/isprsarchives-XXXIX-B5-199-2012 Kang, Z., Tuo, L., Zlatanovab, S., 2012. Continuously deformation monitoring of subway tunnel based on terrestrial point clouds. In: Proceedings of the XXII ISPRS Congress, vol. XXXIX, pp. 199-203.
Adv. Eng. Inform. Koch 29 2 196 2015 10.1016/j.aei.2015.01.008 A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
Tunn. Undergr. Space Technol. Lee 34 61 2013 10.1016/j.tust.2012.11.002 Application and validation of simple image-mosaic technology for interpreting cracks on tunnel lining
Struct. Infrastruct. Eng. Lee 9 6 567 2013 10.1080/15732479.2011.593891 Automated image processing technique for detecting and analysing concrete surface cracks
Linder 2013 Digital Photogrammetry: Theory and Applications
Int. J. Remote Sens. Lu 25 12 2365 2004 10.1080/0143116031000139863 Change detection techniques
Luhmann, T., Robson, S., Kyle, S., Boehm, J., 2016. Close Range Photogrammetry and 3D Imaging.
Lukins, T., Ibrahim, Y., Kaka, A., Trucco, E., 2007. Now you see it: the case for measuring progress with computer vision. In: Proceedings of the 4th International SCRI Research Symposium, pp. 409-422.
10.1109/ICCP.2015.7312681 Makantasis, K., Protopapadakis, E., Doulamis, A., Doulamis, N., Loupos, C., 2015. Deep convolutional neural networks for efficient vision based tunnel inspection. In: Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 335-342. https://doi.org/10.1109/ICCP.2015.7312681.
Martin, H., Chevallier, S., Monacelli, E., 2016. Adaptive visualisation system for construction building information models using saliency. CoRR abs/1603.02028. Available from arXiv:1603.02028. <http://arxiv.org/abs/1603.02028>.
Sensors (Basel, Switzerland) Medina 17 7 1 2017 10.3390/s17071670 Crack detection in concrete tunnels using a gabor filter invariant to rotation
MERMEC, 2014. T-sight 5000. <http://www.mermecgroup.com/northamerica/pageview2.php?i=1028&sl=1>.
Microsoft Research. Image Compositor Editor (ICE). <https://www.microsoft.com/en-us/research/product/computational-photography-applications/image-composite-editor/> (online; accessed December 2017).
10.1016/j.aej.2017.01.020 Mohan, A., Poobal, S. Crack detection using image processing: a critical review and analysis. Alex. Eng. J. https://doi.org/10.1016/j.aej.2017.01.020.
Automat. Constr. Montero 59 99 2015 10.1016/j.autcon.2015.02.003 Past, present and future of robotic tunnel inspection
Muja, M., Lowe, D.G., 2009. Fast approximate nearest neighbors with automatic algorithm configuration. In: Proceedings of the VISAPP International Conference on Computer Vision Theory and Applications, pp. 331-340. <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.1721>.
Orbital Technical Solutions. Unmanned Aircraft Systems. <http://www.orbital-ots.com/drone-services/> (online; accessed June 2018).
Visual Comput. Ortner 32 6-8 859 2016 10.1007/s00371-016-1257-5 Visual analytics and rendering for tunnel crack analysis: a methodological approach for integrating geometric and attribute data
IEEE Trans. Syst., Man, Cybernet. Otsu 9 1 62 1979 10.1109/TSMC.1979.4310076 A threshold selection method from gray-level histograms
Paar, G., Bauer, A., Kontrus, H., 2005. Texture-based fusion between laser scanner and camera for tunnel surface documentation. In: Proceedings of the 7th International Conference on Optical 3-D Measurement Techniques. <http://dibweb.joanneum.at/group_3DVision/3DVision/publications-presentations/literature/pdfs/PUB05DIB005.pdf>.
Pavemetrics TM. Laser Tunnel Scanning System (LTSS). <http://www.pavemetrics.com/wp-content/uploads/2016/03/LTSS_Flyer.pdf> (online; accessed December 2017).
Int. J. Appl. Eng. Res. Pravenaa 11 5 3442 2016 A methodical review on image stitching and video stitching techniques
PRODRONE . Tunnel Inspection Drone. <https://www.prodrone.com/release-en/2845/> (online; accessed June 2018).
10.5194/isprs-annals-III-5-167-2016 Protopapadakis, E., Stentoumis, C., Doulamis, N., Doulamis, A., Loupos, K., Makantasis, K., Kopsiaftis, G., Amditis, A., 2016. Autonomous robotic inspection in tunnels III-5, pp. 167-174. <https://www.researchgate.net/publication/307530827_AUTONOMOUS_ROBOTIC_INSPECTION_IN_TUNNELS>.
Adv. Eng. Inform. Ptrucean 29 2 162 2015 10.1016/j.aei.2015.01.001 State of research in automatic as-built modelling
Qi, D., Liu, Y., Gu, Q., Zheng, F. An algorithm to detect the crack in the tunnel based on the image processing. J. Comput. 26(3). <http://www.csroc.org.tw/journal/JOC26_3/JOC26_3_2.pdf>.
IEEE Trans. Image Process. Radke 14 3 294 2005 10.1109/TIP.2004.838698 Image change detection algorithms: a systematic survey
Earth Sci. Inf. Scaioni 7 2 83 2014 10.1007/s12145-014-0152-8 Photogrammetric techniques for monitoring tunnel deformation
Serra 1983 Image Analysis and Mathematical Morphology
Serra 1983 Image Analysis and Mathematical Morphology
Int. J. Electron. Commun. Technol. (IJECT) Shaskank 05 03 20 2014 A survey and review over image alignment and stitching methods
10.1155/2015/184639 Shen, B., Zhang, W., Qi, D., Wu, X. Wireless multimedia sensor network based subway tunnel crack detection method. Int. J. Distrib. Sensor Networks 11(6). https://doi.org/10.1155/2015/184639.
Stent, S., Gherardi, R., Stenger, B., Soga, K., Cipolla, R., 2013. An Image-Based System for Change Detection on Tunnel Linings, pp. 2-5.
10.22260/ISARC2015/0070 Stent, S., Girerd, C., Long, P., Cipolla, R., 2015. A low-cost robotic system for the efficient visual inspection of tunnels. In: Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, ISARC, pp. 1-8. https://doi.org/10.22260/ISARC2015/0070.
Mach. Vision Appl. J. Stent 27 3 319 2016 10.1007/s00138-014-0648-8 Visual change detection on tunnel linings
Int. J. Eng. Technol. Su 5 4 457 2013 10.7763/IJET.2014.V5.596 Application of computer vision to crack detection of concrete structure
Torok, M., Golparvar-Fard, M., Kochersberger, K. Image-based automated 3d crack detection for post-disaster building assessment 28. <https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CP.1943-5487.0000334>.
Quart. Rep. RTRI Ukai 48 2 94 2007 10.2219/rtriqr.48.94 Advanced inspection system of tunnel wall deformation using image processing
Ukai, M., Nagamine, N. A High-performance Inspection System of Tunnel Wall Deformation Using Continuous Scan Image. Railway Technical Research Institute. <http://www.railway-research.org/IMG/pdf/poster_ukai_masato.pdf>.
van Gosliga, R., Lindenbergh, R., Pfeifer, N., 2006. Deformation analysis of a bored tunnel by means of terrestrial laser scanning. In: Proceedings of the ISPRS Commission V Symposium Image Engineering and Vision Metrology.
Tunn. Undergr. Space Technol. Wang 24 2 136 2009 10.1016/j.tust.2008.05.008 Application and validation of profile-image method for measuring deformation of tunnel wall
Tunn. Undergr. Space Technol. Wang 25 1 78 2010 10.1016/j.tust.2009.09.005 Profile-image method for measuring tunnel profile - improvements and procedures
Wu, Changchang, 2011. VisualSFM: A Visual Structure from Motion System. <http://ccwu.me/vsfm/> (online; accessed December 2017).
J. Comput. Civ. Eng. Yu 31 04016067 2016 10.1061/(ASCE)CP.1943-5487.0000645 Efficient crack detection method for tunnel lining surface cracks based on infrared images
Sensors Zhang 14 10 19307 2014 10.3390/s141019307 Automatic crack detection and classification method for subway tunnel safety monitoring
Comput.-Aided Civ. Infrastruct. Eng. Zhu 31 12 936 2016 10.1111/mice.12230 Panoramic image stitching for arbitrarily shaped tunnel lining inspection
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