{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:46:19Z","timestamp":1773740779415,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2000560"],"award-info":[{"award-number":["2000560"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.<\/jats:p>","DOI":"10.3390\/s20236844","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T10:26:12Z","timestamp":1606731972000},"page":"6844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring"],"prefix":"10.3390","volume":"20","author":[{"given":"Luna","family":"Ngeljaratan","sequence":"first","affiliation":[{"name":"Department of Civil &amp; Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1006-7685","authenticated-orcid":false,"given":"Mohamed A.","family":"Moustafa","sequence":"additional","affiliation":[{"name":"Department of Civil &amp; Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"key":"ref_1","unstructured":"Ngeljaratan, L., and Moustafa, M.A. (2017, January 6). Digital Image Correlation for dynamic shake table test measurements. Proceedings of the 7th International Conference on Advances in Experimental Structural Engineering (7AESE), Pavia, Italy."},{"key":"ref_2","unstructured":"Ngeljaratan, L., and Moustafa, M.A. (2018, January 25\u201329). Novel Digital Image Correlation Instrumentation for Large-Scale Shake Table Tests. Proceedings of the 11th NCEE, Los Angeles, CA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ngeljaratan, L., and Moustafa, M.A. (2019). System Identification of Large-Scale Bridge Model using Digital Image Correlation from Monochrome and Color Cameras. Struct. Health Monit.","DOI":"10.12783\/shm2019\/32467"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fbuil.2019.00085","article-title":"System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation","volume":"5","author":"Ngeljaratan","year":"2019","journal-title":"Front. Built Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110551","DOI":"10.1016\/j.engstruct.2020.110551","article-title":"Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation","volume":"213","author":"Ngeljaratan","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1002\/stc.1819","article-title":"Vision-based multipoint displacement measurement for structural health monitoring","volume":"23","author":"Feng","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16557","DOI":"10.3390\/s150716557","article-title":"A Vision-Based Sensor for Noncontact Structural Displacement Measurement","volume":"15","author":"Feng","year":"2015","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"04015023","DOI":"10.1061\/(ASCE)BE.1943-5592.0000747","article-title":"Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response","volume":"20","author":"Feng","year":"2015","journal-title":"J. Bridg. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e2155","DOI":"10.1002\/stc.2155","article-title":"A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge","volume":"25","author":"Xu","year":"2018","journal-title":"Struct. Control. Health Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1002\/stc.1850","article-title":"Target-free approach for vision-based structural system identification using consumer-grade cameras","volume":"23","author":"Yoon","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2187","DOI":"10.1002\/stc.2187","article-title":"Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation","volume":"25","author":"Acikgoz","year":"2018","journal-title":"Struct. Control. Health Monit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ymssp.2016.11.021","article-title":"Experimental validation of cost-effective vision-based structural health monitoring","volume":"88","author":"Feng","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e1852","DOI":"10.1002\/stc.1852","article-title":"Completely contactless structural health monitoring of real-life structures using cameras and computer vision","volume":"24","author":"Khuc","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1177\/1475921713500513","article-title":"Vision-based monitoring system for evaluating cable tensile forces on a cable-stayed bridge","volume":"12","author":"Kim","year":"2013","journal-title":"Struct. Health Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1177\/1475921713487397","article-title":"Monitoring of a civil structure\u2019s state based on noncontact measurements","volume":"12","author":"Kohut","year":"2013","journal-title":"Struct. Health Monit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1111\/0031-868X.00011","article-title":"Metric Exploitation of still Video Imagery","volume":"15","author":"Fraser","year":"1995","journal-title":"Photogramm. Rec."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"85014","DOI":"10.1088\/0964-1726\/23\/8\/085014","article-title":"Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications","volume":"23","author":"Lynch","year":"2014","journal-title":"Smart Mater. Struct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"263","DOI":"10.12989\/sss.2010.6.3.263","article-title":"Wireless sensor networks for long-term structural health monitoring","volume":"6","author":"Meyer","year":"2010","journal-title":"Smart Struct. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1177\/1475921712462936","article-title":"Compressive sampling\u2013based data loss recovery for wireless sensor networks used in civil structural health monitoring","volume":"12","author":"Bao","year":"2012","journal-title":"Struct. Health Monit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.probengmech.2016.08.001","article-title":"Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery","volume":"46","author":"Huang","year":"2016","journal-title":"Probabilistic Eng. Mech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","article-title":"A lecture on compressive sensing","volume":"24","author":"Baraniuk","year":"2007","journal-title":"J. IEEE Signal Process. Mag."},{"key":"ref_22","first-page":"312","article-title":"Soft computing based compressive sensing techniques in signal processing: A comprehensive review","volume":"30","author":"Mishra","year":"2020","journal-title":"J. Intell. Syst."},{"key":"ref_23","first-page":"21","article-title":"An introduction to compressive sampling a sensing\/sampling paradigm that goes against the common knowledge in data acquisition","volume":"25","author":"Wakin","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1061\/(ASCE)0887-3801(2006)20:6(390)","article-title":"Wavelet-Based Vibration Sensor Data Compression Technique for Civil Infrastructure Condition Monitoring","volume":"20","author":"Zhang","year":"2006","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Perez-Ramirez, C.A., Valtierra-Rodriguez, M., Moreno-Gomez, A., Gonzalez, A.D., Osornio-Rios, R.A., Sanchez, J.P.A., and Romero-Troncoso, R.D.J. (2017, January 8\u201310). Wavelet-based vibration data compression technique for natural frequencies identification of civil infrastructure. Proceedings of the 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico.","DOI":"10.1109\/ROPEC.2017.8261623"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"255","DOI":"10.14257\/ijmue.2015.10.7.27","article-title":"Enhancement of JPEG Compression for GPS Images","volume":"10","author":"Wiseman","year":"2015","journal-title":"Int. J. Multimed. Ubiquitous Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.14704\/nq.2020.18.5.NQ20162","article-title":"The Effect of Re-Use of Lossy JPEG Compression Algorithm on the Quality of Satellite Image","volume":"18","author":"Ameer","year":"2020","journal-title":"NeuroQuantology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"689","DOI":"10.18287\/2412-6179-2016-40-5-689-712","article-title":"Hyperspectral remote sensing data compression and protection","volume":"40","author":"Gashnikov","year":"2016","journal-title":"Comput. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"090902","DOI":"10.1117\/1.OE.59.9.090902","article-title":"Comprehensive review of hyperspectral image compression algorithms","volume":"59","author":"Dua","year":"2020","journal-title":"Opt. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.jvcir.2019.01.042","article-title":"Band reordering heuristics for lossless satellite image compression with 3D-CALIC and CCSDS","volume":"59","author":"Mamun","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Indradjad, A., Nasution, A.S., Gunawan, H., and Widipaminto, A. (2019). A Comparison of Satellite Image Compression Methods in the Wavelet Domain, IOP Publishing.","DOI":"10.1088\/1755-1315\/280\/1\/012031"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/JBHI.2017.2660482","article-title":"High Bit-Depth Medical Image Compression with HEVC","volume":"22","author":"Parikh","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","first-page":"20","article-title":"A new lossless medical image compression technique using hybrid prediction model","volume":"10","author":"Mofreh","year":"2016","journal-title":"Signal Process. Int. J. (SPIJ)"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11554-013-0367-9","article-title":"A novel medical image compression using Ripplet transform","volume":"11","author":"Juliet","year":"2013","journal-title":"J. Real-Time Image Process."},{"key":"ref_35","first-page":"1","article-title":"Medical Image Compression Using View Compensated Wavelet Transform","volume":"9","author":"Sathiyanathan","year":"2018","journal-title":"J. Global Res. Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1111\/0031-868X.00187","article-title":"Effects of Jpeg Compression on the Accuracy of Digital Terrain Models Automatically Derived from Digital Aerial Images","volume":"17","author":"Lam","year":"2001","journal-title":"Photogramm. Rec."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alfio, V.S., Costantino, D., and Pepe, M. (2020). Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry. J. Imaging, 6.","DOI":"10.3390\/jimaging6050030"},{"key":"ref_38","first-page":"847","article-title":"Effects of JPEG compression on the accuracy of photogrammetric point determination","volume":"68","author":"Zhilin","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pss.2016.10.018","article-title":"Effects of image compression and illumination on digital terrain models for the stereo camera of the BepiColombo mission","volume":"136","author":"Re","year":"2017","journal-title":"Planet. Space Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Feng, C., Yu, D., Liang, Y., Guo, D., Wang, Q., and Cui, X. (2019). Assessment of Influence of Image Processing on Fully Automatic UAV Photogrammetry. ISPRS-Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci., 269\u2013275.","DOI":"10.5194\/isprs-archives-XLII-2-W13-269-2019"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mar\u010di\u0161, M., and Fra\u0161tia, M. (2018). Influence of image compression on image and reference point accuracy in photogrammetric measurement. Advances and Trends in Geodesy, Cartography and Geoinformatics, Proceedings of the 10th International Scientific and Professional Conference on Geodesy, Cartography and Geoinformatics (GCG 2017), Dem\u00e4novsk\u00e1 Dolina, Low Tatras, Slovakia 10\u201313 October 2017, CRC Press.","DOI":"10.1201\/9780429505645-13"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Motta, G., Rizzo, F., and Storer, J.A. (2006). Hyperspectral Data Compression, Springer Science and Business Media LLC.","DOI":"10.1007\/0-387-28600-4"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7431","DOI":"10.1109\/TGRS.2016.2603998","article-title":"Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression","volume":"54","author":"Conoscenti","year":"2016","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1364\/JOSAA.34.002170","article-title":"Hyperspectral image compression approaches: Opportunities, challenges, and future directions: Discussion","volume":"34","author":"Dusselaar","year":"2017","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guerra, R., Barrios, Y., D\u00edaz, M., Santos, L., Lopez, S., and Sarmiento, R. (2018). A New Algorithm for the On-Board Compression of Hyperspectral Images. Remote Sens., 10.","DOI":"10.3390\/rs10030428"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2016). Real-Time Progressive Hyperspectral Image Processing, Springer Science and Business Media LLC.","DOI":"10.1007\/978-1-4419-6187-7"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.patrec.2018.09.013","article-title":"Hyperspectral image compression based on simultaneous sparse representation and general-pixels","volume":"116","author":"Fu","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1109\/30.920468","article-title":"The JPEG2000 still image coding system: An overview","volume":"46","author":"Christopoulos","year":"2000","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TMM.2017.2741426","article-title":"Lossless Compression of Color Filter Array Mosaic Images with Visualization via JPEG 2000","volume":"20","author":"Marcellin","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1117\/1.1469618","article-title":"JPEG2000: Image Compression Fundamentals, Standards and Practice","volume":"11","author":"Taubman","year":"2002","journal-title":"J. Electron. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Marlapalli, K., Bandlamudi, R.S.B.P., Busi, R., Pranav, V., and Madhavrao, B. (2020). A Review on Image Compression Techniques, Springer Science and Business Media LLC.","DOI":"10.1007\/978-981-15-5397-4_29"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"164933","DOI":"10.1016\/j.ijleo.2020.164933","article-title":"Developing a compression procedure based on the wavelet denoising and JPEG2000 compression","volume":"218","author":"Gungor","year":"2020","journal-title":"Optik"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s11207-016-1038-3","article-title":"JPEG2000 Image Compression on Solar EUV Images","volume":"292","author":"Fischer","year":"2016","journal-title":"Sol. Phys."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Radosavljevi\u0107, M., Brklja\u010d, B., Lugonja, P., \u0106rnojevic, V., Trpovski, \u017d., Xiong, Z., and Vukobratovi\u0107, D. (2020). Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study. Remote Sens., 12.","DOI":"10.3390\/rs12101590"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Caba, J., D\u00edaz, M., Barba, J., Guerra, R., and L\u00f3pez, J.A.D.L.T.A.S. (2020). FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations. Remote Sens., 12.","DOI":"10.3390\/rs12223741"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"B\u00e1scones, D., Gonz\u00e1lez, C., and Mozos, D. (2018). Hyperspectral image compression using vector quantization, PCA and JPEG2000. Remote Sens., 10.","DOI":"10.3390\/rs10060907"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"B\u00e1scones, D., Gonz\u00e1lez, C., and Mozos, D. (2020). An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12162563"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2006.888109","article-title":"Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Machidon, A.L., Del Frate, F., Picchiani, M., Machidon, O.M., and Ogrutan, P.L. (2020). Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12111698"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s11554-016-0650-7","article-title":"FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images","volume":"16","author":"Fernandez","year":"2019","journal-title":"J. Real-Time Image Process."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mei, S., Khan, B.M., Zhang, Y., and Du, Q. (2018, January 22\u201327). Low-Complexity Hyperspectral Image Compression Using Folded PCA and JPEG2000. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519455"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"061507","DOI":"10.1117\/1.JRS.6.061507","article-title":"Graphics processing unit implementation of JPEG2000 for hyperspectral image compression","volume":"6","author":"Ciznicki","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10596-019-09855-2","article-title":"Hybrid hyperspectral image compression technique for non-iterative factorized tensor decomposition and principal component analysis: Application for NASA\u2019s AVIRIS data","volume":"23","author":"Jeyakumar","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1080\/22797254.2018.1441670","article-title":"Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images","volume":"51","author":"Licciardi","year":"2018","journal-title":"Eur. J. Remote. Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/5.364461","article-title":"Neural network approaches to image compression","volume":"83","author":"Dony","year":"1995","journal-title":"Proc. IEEE"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/S0923-5965(98)00041-1","article-title":"Image compression with neural networks\u2014A survey","volume":"14","author":"Jiang","year":"1999","journal-title":"Signal Process. Image Commun."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1109\/TCSVT.2019.2910119","article-title":"Image and Video Compression with Neural Networks: A Review","volume":"30","author":"Ma","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e1856","DOI":"10.1002\/stc.1856","article-title":"Robust data transmission and recovery of images by compressed sensing for structural health diagnosis","volume":"24","author":"Yang","year":"2016","journal-title":"Struct. Control. Health Monit."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"107061","DOI":"10.1016\/j.ymssp.2020.107061","article-title":"Recovering compressed images for automatic crack segmentation using generative models","volume":"146","author":"Huang","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e2313","DOI":"10.1002\/stc.2313","article-title":"Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network","volume":"26","author":"Xu","year":"2019","journal-title":"Struct. Control. Health Monit."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"457","DOI":"10.7763\/IJET.2014.V5.596","article-title":"Application of Computer Vision to Crack Detection of Concrete Structure","volume":"5","author":"Su","year":"2013","journal-title":"Int. J. Eng. Technol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e2075","DOI":"10.1002\/stc.2075","article-title":"Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images","volume":"25","author":"Li","year":"2018","journal-title":"Struct. Control. Health Monit."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s40799-019-00358-4","article-title":"Dynamic Deformation Measurement by the Sampling Moir\u00e9 Method from Video Recording and its Application to Bridge Engineering","volume":"44","author":"Ri","year":"2020","journal-title":"Exp. Tech."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ymssp.2019.04.031","article-title":"Compressed sensing for OMA using full-field vibration images","volume":"129","author":"Chang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"35","DOI":"10.26833\/ijeg.287308","article-title":"\u00d6zg\u00fcr the Effect of Jpeg Compression in Close Range Photogrammetry","volume":"2","author":"Akcay","year":"2017","journal-title":"Int. J. Eng. Geosci."},{"key":"ref_76","unstructured":"Schneider, C.T. (1991, January 14\u201317). 3-D Vermessung von Oberfl\u00e4chen und Bauteilen durch Photogrammetrie und Bildverarbeitung. Proceedings of the IDENT\/VISION\u201991, Stuttgart, Germany."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/JRA.1987.1087109","article-title":"A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses","volume":"3","author":"Tsai","year":"1987","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_78","unstructured":"Heikkila, J., and Silven, O. (1997, January 17\u201319). A four-step camera calibration procedure with implicit image correction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA."},{"key":"ref_79","first-page":"129","article-title":"An overview of software in non-topographic photogrammetry","volume":"Volume 10","author":"Karara","year":"1989","journal-title":"Non-Topographic Photogrammetry"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0924-2716(97)00005-1","article-title":"Digital camera self-calibration","volume":"52","author":"Fraser","year":"1997","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Gruen, A., and Beyer, H.A. (2001). System Calibration through Self-Calibration, Springer Science and Business Media LLC.","DOI":"10.1007\/978-3-662-04567-1_7"},{"key":"ref_82","unstructured":"Miller, E. (2020, October 05). How Not to Sort by Average Rating [EB\/OL]. Available online: https:\/\/www.evanmiller.org\/how-not-to-sort-by-average-rating.html."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/mice.12338","article-title":"Structural Displacement Measurement Using an Unmanned Aerial System","volume":"33","author":"Yoon","year":"2018","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"04020045","DOI":"10.1061\/(ASCE)CP.1943-5487.0000928","article-title":"Methodology and Validation of UAV-Based Video Analysis Approach for Tracking Earthquake-Induced Building Displacements","volume":"34","author":"Wang","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_85","first-page":"855","article-title":"Close-range camera calibration","volume":"37","author":"Duane","year":"1971","journal-title":"Photogramm. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/0141-0296(93)90054-8","article-title":"Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge","volume":"15","author":"Stephen","year":"1993","journal-title":"Eng. Struct."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Mahajan, S.H., and Harpale, V.K. (2015, January 26\u201327). Adaptive and Non-adaptive Image Interpolation Techniques. Proceedings of the 2015 International Conference on Computing Communication Control and Automation, Pune, India.","DOI":"10.1109\/ICCUBEA.2015.154"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/TMI.1983.4307610","article-title":"Comparison of Interpolating Methods for Image Resampling","volume":"2","author":"Parker","year":"1983","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/83.136601","article-title":"Image compression using the 2-D wavelet transform","volume":"1","author":"Lewis","year":"1992","journal-title":"IEEE Trans. Image Process."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1049\/el:19900766","article-title":"VLSI architecture for the discrete wavelet transform","volume":"26","author":"Knowles","year":"1990","journal-title":"Electron. Lett."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1049\/el:19910110","article-title":"VLSI architecture for 2-D Daubechies wavelet transform without multipliers","volume":"27","author":"Lewis","year":"1991","journal-title":"Electron. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Devaraj, S.J. (2019). Emerging Paradigms in Transform-Based Medical Image Compression for Telemedicine Environment, Elsevier BV.","DOI":"10.1016\/B978-0-12-816948-3.00002-7"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"238","DOI":"10.5201\/ipol.2011.g_lmii","article-title":"Linear Methods for Image Interpolation","volume":"1","author":"Getreuer","year":"2011","journal-title":"Image Process. Line"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1109\/78.258085","article-title":"Embedded image coding using zerotrees of wavelet coefficients","volume":"41","author":"Shapiro","year":"1993","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/76.499834","article-title":"A new, fast, and efficient image codec based on set partitioning in hierarchical trees","volume":"6","author":"Said","year":"1996","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1117\/12.334677","article-title":"Embedded and efficient low-complexity hierarchical image coder","volume":"3653","author":"Islam","year":"1998","journal-title":"Proc. SPIE"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1117\/1.602573","article-title":"Lossy image codec based on adaptively scanned wavelet difference reduction","volume":"39","author":"Walker","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_98","unstructured":"Tian, J., and Wells, J.R.O. (April, January 31). A lossy image codec based on index coding. Proceedings of the Data Compression Conference\u2014DCC \u201996, Snowbird, UT, USA."},{"key":"ref_99","unstructured":"(2020, October 05). TRITOP, Optical 3D Coordinate Measuring Machine, GOM. Available online: https:\/\/trilion.com\/wp-content\/uploads\/TRITOP-Coordinate-Measuring-System.pdf."},{"key":"ref_100","unstructured":"Yeow, T.Z., Kusunoki, K., Nakamura, I., Hibino, Y., Ohkubo, T., Seike, T., Yagi, S., Mukai, T., Calvi, P., and Moustafa, M. (2020, January 14\u201318). The 2019 Tokyo Metropolitan Resilience Project E-Defense Test of a 3-Story Disaster Management Center. Proceedings of the 17th World Conference on Earthquake Engineering, Sendai, Japan."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6844\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:39:34Z","timestamp":1760179174000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":100,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236844"],"URL":"https:\/\/doi.org\/10.3390\/s20236844","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}