Gotowa bibliografia na temat „Remote photoplethysmography”
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Artykuły w czasopismach na temat "Remote photoplethysmography"
van Gastel, Mark, Sander Stuijk i Gerard de Haan. "Robust respiration detection from remote photoplethysmography". Biomedical Optics Express 7, nr 12 (3.11.2016): 4941. http://dx.doi.org/10.1364/boe.7.004941.
Pełny tekst źródłaLaurie, Jordan, Niall Higgins, Thierry Peynot i Jonathan Roberts. "Dedicated Exposure Control for Remote Photoplethysmography". IEEE Access 8 (2020): 116642–52. http://dx.doi.org/10.1109/access.2020.3003548.
Pełny tekst źródłaKim, Seung-Hyun, Su-Min Jeon i Eui Chul Lee. "Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal". Sensors 22, nr 8 (16.04.2022): 3070. http://dx.doi.org/10.3390/s22083070.
Pełny tekst źródłaBoccignone, Giuseppe, Donatello Conte, Vittorio Cuculo, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti i Edoardo Mortara. "pyVHR: a Python framework for remote photoplethysmography". PeerJ Computer Science 8 (15.04.2022): e929. http://dx.doi.org/10.7717/peerj-cs.929.
Pełny tekst źródłaBobbia, Serge, Richard Macwan, Yannick Benezeth, Alamin Mansouri i Julien Dubois. "Unsupervised skin tissue segmentation for remote photoplethysmography". Pattern Recognition Letters 124 (czerwiec 2019): 82–90. http://dx.doi.org/10.1016/j.patrec.2017.10.017.
Pełny tekst źródłaPo, Lai-Man, Litong Feng, Yuming Li, Xuyuan Xu, Terence Chun-Ho Cheung i Kwok-Wai Cheung. "Block-based adaptive ROI for remote photoplethysmography". Multimedia Tools and Applications 77, nr 6 (13.03.2017): 6503–29. http://dx.doi.org/10.1007/s11042-017-4563-7.
Pełny tekst źródłaPeng, Rong-Chao, Wen-Rong Yan, Ning-Ling Zhang, Wan-Hua Lin, Xiao-Lin Zhou i Yuan-Ting Zhang. "Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography". Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6830152.
Pełny tekst źródłaLee, Kunyoung, Jaemu Oh, Hojoon You i Eui Chul Lee. "Improving Remote Photoplethysmography Performance through Deep-Learning-Based Real-Time Skin Segmentation Network". Electronics 12, nr 17 (4.09.2023): 3729. http://dx.doi.org/10.3390/electronics12173729.
Pełny tekst źródłaBok, Jin Yeong, Kun Ha Suh i Eui Chul Lee. "Detecting Fake Finger-Vein Data Using Remote Photoplethysmography". Electronics 8, nr 9 (11.09.2019): 1016. http://dx.doi.org/10.3390/electronics8091016.
Pełny tekst źródłaYu, Su-Gyeong, So-Eui Kim, Na Hye Kim, Kun Ha Suh i Eui Chul Lee. "Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals". Sensors 21, nr 18 (17.09.2021): 6241. http://dx.doi.org/10.3390/s21186241.
Pełny tekst źródłaRozprawy doktorskie na temat "Remote photoplethysmography"
Soleimani, Vahid. "Remote depth-based photoplethysmography in pulmonary function testing". Thesis, University of Bristol, 2018. http://hdl.handle.net/1983/f6a6f7b6-943f-43f7-b684-1612161aee1a.
Pełny tekst źródłaBotina, Monsalve Deivid. "Remote photoplethysmography measurement and filtering using deep learning based methods". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2022. http://www.theses.fr/2022UBFCK061.
Pełny tekst źródłaRPPG is a technique developed to measure the blood volume pulse signal and then estimate physiological data such as pulse rate, breathing rate, and pulse rate variability.Due to the multiple sources of noise that deteriorate the quality of the RPPG signal, conventional filters are commonly used. However, some alterations remain, but interestingly, an experienced eye can easily identify them. In the first part of this thesis, we propose the Long Short-Term Memory Deep-Filter (LSTMDF) network in the RPPG filtering task. We use different protocols to analyze the performance of the method. We demonstrate how the network can be efficiently trained with a few signals. Our study demonstrates experimentally the superiority of the LSTM-based filter compared with conventional filters. We found a network sensitivity related to the average signal-to-noise ratio on the RPPG signals.Approaches based on convolutional networks such as 3DCNNs have recently outperformed traditional hand-crafted methods in the RPPG measurement task. However, it is well known that large 3DCNN models have high computational costs and may be unsuitable for real-time applications. As the second contribution of this thesis, we propose a study of a 3DCNN architecture, finding the best compromise between pulse rate measurement precision and inference time. We use an ablation study where we decrease the input size, propose a custom loss function, and evaluate the impact of different input color spaces. The result is the Real-Time RPPG (RTRPPG), an end-to-end RPPG measurement framework that can be used in GPU and CPU. We also proposed a data augmentation method that aims to improve the performance of deep learning networks when the database has specific characteristics (e.g., fitness movement) and when there is not enough data available
Zaunseder, Sebastian, Alexander Trumpp, Hannes Ernst, Michael Förster i Hagen Malberg. "Spatio-temporal analysis of blood perfusion by imaging photoplethysmography". SPIE, 2018. https://tud.qucosa.de/id/qucosa%3A35157.
Pełny tekst źródłaTrumpp, Alexander, Johannes Lohr, Daniel Wedekind, Martin Schmidt, Matthias Burghardt, Axel R. Heller, Hagen Malberg i Sebastian Zaunseder. "Camera-based photoplethysmography in an intraoperative setting". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234950.
Pełny tekst źródłaUggla, Lingvall Kristoffer. "Remote heart rate estimation by evaluating measurements from multiple signals". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210303.
Pełny tekst źródłaEn människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.
Ghanadian, Hamideh. "A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using RGB Camera". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38563.
Pełny tekst źródłaAlghoul, Karim. "Heart Rate Variability Extraction from Video Signals". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33003.
Pełny tekst źródłaTrumpp, Alexander. "Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography". Doctoral thesis, 2019. https://tud.qucosa.de/id/qucosa%3A36758.
Pełny tekst źródłaCzęści książek na temat "Remote photoplethysmography"
Lempe, Georg, Sebastian Zaunseder, Tom Wirthgen, Stephan Zipser i Hagen Malberg. "ROI Selection for Remote Photoplethysmography". W Bildverarbeitung für die Medizin 2013, 99–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36480-8_19.
Pełny tekst źródłaHe, Lin, Kazi Shafiul Alam, Jiachen Ma, Richard Povinelli i Sheikh Iqbal Ahamed. "Dynamics Reconstruction of Remote Photoplethysmography". W Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 96–110. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99194-4_8.
Pełny tekst źródłaKalinin, Konstantin, Yuriy Mironenko, Mikhail Kopeliovich i Mikhail Petrushan. "Towards Collecting Big Data for Remote Photoplethysmography". W Lecture Notes in Networks and Systems, 70–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80129-8_6.
Pełny tekst źródłaKalinin, Konstantin, Yuriy Mironenko, Mikhail Kopeliovich i Mikhail Petrushan. "Towards Collecting Big Data for Remote Photoplethysmography". W Lecture Notes in Networks and Systems, 70–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80129-8_6.
Pełny tekst źródłaLiu, Siqi, Pong C. Yuen, Shengping Zhang i Guoying Zhao. "3D Mask Face Anti-spoofing with Remote Photoplethysmography". W Computer Vision – ECCV 2016, 85–100. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46478-7_6.
Pełny tekst źródłaMonika, Harish Kumar, Sakshi Kaushal i Varinder Garg. "Remote Photoplethysmography: Digital Disruption in Health Vital Acquisition". W Explainable Machine Learning for Multimedia Based Healthcare Applications, 215–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38036-5_12.
Pełny tekst źródłaQiu, Zhaolin, Lanfen Lin, Hao Sun, Jiaqing Liu i Yen-Wei Chen. "Artificial Intelligence in Remote Photoplethysmography: Remote Heart Rate Estimation from Video Images". W Handbook of Artificial Intelligence in Healthcare, 267–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79161-2_11.
Pełny tekst źródłaZhang, Haoyu, Raghavendra Ramachandra i Christoph Busch. "Face Presentation Attack Detection Using Remote Photoplethysmography Transformer Model". W Communications in Computer and Information Science, 558–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31417-9_42.
Pełny tekst źródłaSinhal, Ruchika, Kavita Singh i M. M. Raghuwanshi. "An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring". W Computer Vision and Machine Intelligence in Medical Image Analysis, 21–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_3.
Pełny tekst źródłaLee, Kunyoung, Hojoon You, Jaemu Oh i Eui Chul Lee. "Extremely Lightweight Skin Segmentation Networks to Improve Remote Photoplethysmography Measurement". W Intelligent Human Computer Interaction, 454–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27199-1_45.
Pełny tekst źródłaStreszczenia konferencji na temat "Remote photoplethysmography"
Mironenko, Yuriy, Konstantin Kalinin, Mikhail Kopeliovich i Mikhail Petrushan. "Remote Photoplethysmography: Rarely Considered Factors". W 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00156.
Pełny tekst źródłaMacwan, Richard, Yannick Benezeth, Alamin Mansouri, Keisuke Nakamura i Randy Gomez. "Remote Photoplethysmography measurement using constrained ICA". W 2017 E-Health and Bioengineering Conference (EHB). IEEE, 2017. http://dx.doi.org/10.1109/ehb.2017.7995453.
Pełny tekst źródłaWang, Wenjin, Albertus C. den Brinker, Sander Stuijk i Gerard de Haan. "Color-Distortion Filtering for Remote Photoplethysmography". W 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017. http://dx.doi.org/10.1109/fg.2017.18.
Pełny tekst źródłaDemirezen, Halil, i Cigdem Eroglu Erdem. "Remote Photoplethysmography Using Nonlinear Mode Decomposition". W ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462538.
Pełny tekst źródłaHarbawi, Malek A., Muhammad I. Ibrahimy i S. M. A. Motakabber. "Photoplethysmography based remote health monitoring system". W 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE, 2013. http://dx.doi.org/10.1109/icsima.2013.6717955.
Pełny tekst źródłaRubins, Uldis, Zbignevs Marcinkevics, Robert Andrianirina Muckle, Ieva Henkuzena, Andris Roze i Andris Grabovskis. "Remote photoplethysmography for assessment of oral mucosa". W Preclinical and Clinical Optical Diagnostics, redaktorzy J. Quincy Brown i Ton G. van Leeuwen. SPIE, 2019. http://dx.doi.org/10.1117/12.2526979.
Pełny tekst źródłaMarcinkevics, Zbignevs, Kapil Ilango, Paula Balode, Uldis Rubins i Andris Grabovskis. "The assessment of gingivitis using remote photoplethysmography". W Third International Conference Biophotonics Riga 2020, redaktor Janis Spigulis. SPIE, 2020. http://dx.doi.org/10.1117/12.2581969.
Pełny tekst źródłaFeng, Litong, Lai-Man Po, Xuyuan Xu i Yuming Li. "Motion artifacts suppression for remote imaging photoplethysmography". W 2014 International Conference on Digital Signal Processing (DSP). IEEE, 2014. http://dx.doi.org/10.1109/icdsp.2014.6900813.
Pełny tekst źródłaWu, Bing-Fei, Po-Wei Huang, Da-Hong He, Chung-Han Lin i Kuan-Hung Chen. "Remote Photoplethysmography Enhancement with Machine Leaning Methods". W 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914554.
Pełny tekst źródłaKossack, Benjamin, Eric Wisotzky, Peter Eisert, Sebastian P. Schraven, Brigitta Globke i Anna Hilsmann. "Perfusion assessment via local remote photoplethysmography (rPPG)". W 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00238.
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