يعرض 1 - 20 نتائج من 141 نتيجة بحث عن '"Vision Based Measurement"', وقت الاستعلام: 0.75s تنقيح النتائج
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    المصدر: Ingeniería; Vol. 29 No. 1 (2024): January-April; e20057 ; Ingeniería; Vol. 29 Núm. 1 (2024): Enero-Abril; e20057 ; 2344-8393 ; 0121-750X

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Weems, S. C. Kienle, and A. M. Sims, “Three-dimensional acceleration measurement using videogrammetry tracking data,” Experimental Mechanics, vol. 51, no. 2, pp. 199–217, 2011. https://doi.org/10.1007/s11340-010-9352-4; F. Karimirad, S. Chauhan, and B. Shirinzadeh, “Vision-based force measurement using neural networks for biological cell mi-croinjection,” Journal of Biomechanics, vol. 47, no. 5, pp. 1157–1163, 2014. https://doi.org/10.1016/j.jbiomech.2013.12.007; Y. Fukuda, M. Q. Feng, and M. Shinozuka, “Cost-effective vision-based system for monitoring dynamic response of civil engineer-ing structures,” Structural Control and Health Monitoring, vol. 17, no. 8, pp. 918–936, 2010. https://doi.org/10.1002/stc.360; S. Benameur, M. Mignotte, and F. 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Sohn, “Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements,” Mechanical Systems and Signal Processing, vol. 42, no. 1-2, pp. 194–205, 2014. https://doi.org/10.1016/j.ymssp.2013.09.014; F. Zheng, L. Shao, V. Racic, and J. Brownjohn, “Measuring human-induced vibrations of civil engineering structures via vision-based motion tracking,” Measurement, vol. 83, pp. 44–56, 2016. https://doi.org/10.1016/j.measurement.2016.01.015; D. Ribeiro, R. Calçada, J. Ferreira, and T. Martins, “Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system,” Engineering Structures, vol. 75, pp. 164–180, 2014. https://doi.org/10.1016/j.engstruct.2014.04.051; D. Béréziat and I. Herlin, “Motion and acceleration from image assimilation with evolution models,” Digital Signal Processing, vol. 83, pp. 45–58, 2018. https://doi.org/10.1016/j.dsp.2018.08.008; A. Y. Sun, D. Wang, and X. 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Yousuf, “Novel method to assess motion blur kernel parameters and comparative study of restoration techniques using different image layouts,” in 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), IEEE, 2016, pp. 367–372. https://doi.org/10.1109/ICIEV.2016.7760027; H.-Y. Lin, “Vehicle speed detection and identification from a single motion blurred image,” in Application of Computer Vision, 2005. WACV/MOTIONS’05 Volume 1. Seventh IEEE Workshops on, IEEE, vol. 1, 2005, pp. 461–467.; M. Celestino and O. Horikawa, “Velocity measurement based on image blur,” Computer graphics and image processing, vol. 3, pp. 633–642, 2008.; H. Pazhoumand-Dar, A. M. T. Abolhassani, and E. Saeedi, “Object speed estimation by using fuzzy set,” World Academy of Sci-ence, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineer-ing, vol. 4, no. 4, pp. 688–691, 2010.; S. Rezvankhah, A. A. Bagherzadeh, H. Moradi, and B. N. A. Member, “A Real-time Velocity Estimation using Motion Blur in Air Hockey,” in 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2012, pp. 1767-1772, https://doi.org/10.1109/ROBIO.2012.6491223; J. Mohammadi and A. Taherkhani, “Object Speed Estimation in Frequency Domain of Single Taken Image,” Journal of Basic and Applied Scientific Research, vol. 3, pp. 120-124, 2013.; S. McCloskey, Y. Ding, and J. Yu, “Design and estimation of coded exposure point spread functions,” IEEE transactions on pat-tern analysis and machine intelligence, vol. 34, no. 10, p. 2071, 2012. https://doi.org/10.1109/TPAMI.2012.108; A. Agrawal, Y. Xu, and R. Raskar, “Invertible motion blur in video,” in ACM Transactions on Graphics (TOG), vol. 28, 2009, p. 95. https://doi.org/10.1145/1531326.1531401; M. Lee, K.-S. Kim, and S. Kim, “Measuring vehicle velocity in real time using modulated motion blur of camera image data,” IEEE Transactions on Vehicular Technology, vol. 66, no. 5, pp. 3659–3673, 2016. https://doi.org/10.1109/TVT.2016.2600281; M. Lee, “A study on measuring vehicle velocity in real time using modulated motion blur of camera image data,” Ph.D. disser-tation, Korea Advanced Institute of Science and Technology, 2017. https://doi.org/10.1109/TVT.2016.2600281; M. Lee, K.-S. Kim, J. Cho, and S. Kim, “Development of a vehicle body velocity sensor using modulated motion blur,” in 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 2017, pp. 406–411. https://doi.org/10.1109/AIM.2017.8014051; J. Jing, F. Xiao, L. Yang, S. Wang, and B. Yu, “Measurements of velocity field and diameter distribution of particles in multi-phase flow based on trajectory imaging,” Flow Measurement and Instrumentation, vol. 59, pp. 103–113, 2018. https://doi.org/10.1016/j.flowmeasinst.2017.12.005; K. Matsuo and T. Yakoh, “Position and velocity measurement method from a single image using modulated illumination,” in 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC), IEEE, 2018, pp. 353–359. https://doi.org/10.1109/AMC.2019.8371117; J. A. Dwicahya, N. Ramadijanti, and A. Basuki, “Moving object velocity detection based on motion blur on photos using gray level,” in 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), IEEE, 2018, pp. 192–198. https://doi.org/10.1109/KCIC.2018.8628598; J. A. Cortes-Osorio, J. B. Gomez-Mendoza, and J. C. Riano-Rojas, “Velocity estimation from a single linear motion blurred image using discrete cosine transform,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 4038–4050, 2019. https://doi.org/10.1109/TIM.2018.2882261; J. A. Cortes-Osorio, “A contribution to the estimation of kinematic quantities from linear motion blurred images,” Ph.D. disser-tation, Universidad Nacional de Colombia Sede Manizales, 2020.; Q. Guo et al., “Learning to Adversarially Blur Visual Object Tracking,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. https://doi.org/10.1109/ICCV48922.2021.01066; M. Li, H. Du, Q. Zhang, and J. Wang, “Improved particle image velocimetry through cell segmentation and competitive surviv-al,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 6, pp. 1221–1229, 2008. https://doi.org/10.1109/TIM.2007.915443; X. Liu and J. Katz, “Instantaneous pressure and material acceleration measurements using a four-exposure piv system,” Experi-ments in Fluids, vol. 41, no. 2, p. 227, 2006. https://doi.org/10.1007/s00348-006-0152-7; H. Zhou, M. Chen, L. Zhang, N. Ye, and C. Tao, “Measuring shape and motion of a high-speed object with designed features from motion blurred images,” Measurement, vol. 145, pp. 559–567, 2019. https://doi.org/10.1016/j.measurement.2019.05.023; L. Li, T. Martin, and X. Xu, “A novel vision-based real-time method for evaluating postural risk factors associated with musculo-skeletal disorders,” Appl. Ergon., vol. 87, p. 103 138, 2020. https://doi.org/10.1016/j.apergo.2020.103138; C. Yu, X. Bi, and Y. Fan, “Deep learning for fluid velocity field estimation: A review,” Ocean Engineering, vol. 271, p. 113 693, 2023. https://doi.org/10.1016/j.oceaneng.2023.113693; P. Chu, B. T. Wolfe, and Z. Wang, “Measurement of incandescent microparticle acceleration using stereoscopic imaging,” Review of Scientific Instruments, vol. 89, no. 10, 2018. https://doi.org/10.1063/1.5034311; G. Chen, L. Li, C. Zhao, R. Huang, and F. Guo, “Acceleration characteristics of a rockslide using the particle image velocimetry technique,” Journal of Sensors, vol. 2016, 2016. https://doi.org/10.1155/2016/2650871; D. C. Luvizon, B. T. Nassu, and R. Minetto, “A video-based system for vehicle speed measurement in urban roadways,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1393–1404, 2017. https://doi.org/10.1109/TITS.2016.2600281; J. X. Wang, “Research of vehicle speed detection algorithm in video surveillance,” in Audio, Language and Image Processing (ICALIP), 2016 International Conference on, IEEE, 2016, pp. 349–352. https://doi.org/10.1109/ICALIP.2016.7846482; K. V. K. Kumar, P. Chandrakant, S. Kumar, and K. J. Kushal, “Vehicle speed detection using corner detection,” in 2014 Fifth International Conference on Signal and Image Processing, 2014, pp. 253–258. https://doi.org/10.1109/ICSIP.2014.46; J. Dong, Y. Song, H. Wang, J. Zeng, and Z. Wu, “Predicting flow velocity affected by seaweed resistance using svm regression,” in Computer Application and System Modeling (ICCASM), 2010 International Conference on, vol. 2, 2010, pp. V2–273. https://doi.org/10.1109/ICCASM.2010.5620588; O. Genç and A. Dag˘, “A machine learning-based approach to predict the velocity profiles in small streams,” Water Resources Management, vol. 30, no. 1, pp. 43–61, 2016. https://doi.org/10.1007/s11269-015-1123-7; M. Morimoto, K. Fukami, and K. Fukagata, “Experimental velocity data estimation for imperfect particle images using machine learning,” arXiv preprint arXiv:2005.00756, 2020. https://doi.org/10.1063/5.0060760; P. J. Chun, T. Yamane, S. Izumi, and N. Kuramoto, “Development of a machine learning-based damage identification method using multipoint simultaneous acceleration measurement results,” Sensors, vol. 20, no. 10, p. 2780, 2020. https://doi.org/10.3390/s20102780; W. Kim, M. Tanaka, M. Okutomi, and Y. Sasaki, “Learning-based human segmentation and velocity estimation using automatic labeled lidar sequence for training,” IEEE Access, vol. 8, pp. 88 443–88 452, 2020. https://doi.org/10.1109/ACCESS.2020.2993299; B. Major, D. Fontijne, A. Ansari, et al., “Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019, pp. 924–932. https://doi.org/10.1109/ICCVW.2019.00121; C. Guo, Y. Fan, C. Yu, Y. Han, and X. Bi, “Time-resolved particle image velocimetry algorithm based on deep learning,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, 2022. https://doi.org/10.1109/TIM.2022.3141750; S. Cai, J. 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    المؤلفون: Rinaldi, C., Lepidi, M., Gattulli, V.

    المساهمون: Rinaldi, C., Lepidi, M., Gattulli, V.

    Relation: info:eu-repo/semantics/altIdentifier/isbn/9781644902431; ispartofbook:Theoretical and Applied Mechanics – AIMETA 2022; XXV National Congress of the Italian Association of Theoretical and Applied Mechanics, AIMETA 2022; volume:26; firstpage:485; lastpage:491; numberofpages:7; https://hdl.handle.net/11573/1684286; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85152679811

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    المساهمون: Giulietti, Nicola, Chiariotti, Paolo, Revel, Gian Marco

    Relation: info:eu-repo/semantics/altIdentifier/pmid/37112364; info:eu-repo/semantics/altIdentifier/wos/WOS:000979294000001; volume:23; issue:8; firstpage:4023; lastpage:4047; numberofpages:25; journal:SENSORS; https://hdl.handle.net/11311/1238598; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85153967727

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    المصدر: Structural Safety and Reliability: Proceedings of the Japan Conference on Structural Safety and Reliability (JCOSSAR). 2023, :54

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    المساهمون: Fedullo, T., Cassanelli, D., Gibertoni, G., Tramarin, F., Quaranta, L., Riva, I., Tanga, L., Oddone, F., Rovati, L.

    Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000844142300016; volume:71; firstpage:1; lastpage:1; journal:IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT; https://hdl.handle.net/11380/1287177; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85135766113

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    المساهمون: Fedullo, T, Cassanelli, D, Gibertoni, G, Tramarin, F, Quaranta, L, de Angelis, G, Rovati, L

    وصف الملف: ELETTRONICO

    Relation: info:eu-repo/semantics/altIdentifier/isbn/978-1-7281-9539-1; info:eu-repo/semantics/altIdentifier/wos/WOS:000825383600152; ispartofbook:2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 2021 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2021; firstpage:1; lastpage:6; numberofpages:6; serie:CONFERENCE PROCEEDINGS - IEEE INSTRUMENTATION/MEASUREMENT TECHNOLOGY CONFERENCE; https://hdl.handle.net/11585/922148; https://ieeexplore.ieee.org/document/9459946