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Gerhard de Haan Freie Universität Berlin | FUB · Institut Futur; Department of Education an Psychology Prof. Dr. Connect with experts in your field Join ResearchGate to contact this researcher.
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Gerard de Haan is Professor of Electronic Systems within the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). His research field is video signal processing in a broad sense. Application areas include multimedia systems and video health monitoring.
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PMID: 23744659 DOI: 10.1109/TBME.2013.2266196 Abstract Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success.
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Andreia V. Moço, Sander Stuijk & Gerard de Haan Philips Innovation Group, Philips Research, High Tech Campus 36, 5656AE, Eindhoven, The Netherlands Gerard de Haan
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Epub 2019 Aug 30. Authors Wenjin Wang, Albertus C den Brinker, Gerard de Haan PMID: 31484105 DOI: 10.1109/TBME.2019.2938564 Abstract Near-infrared (NIR) remote photoplethysmography (PPG) promises attractive applications in darkness, as it involves unobtrusive, invisible light.
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For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement (±1.96σ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98%.
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Gerard de Haan received the B.Sc., M.Sc., and Ph.D. degrees from Delft University of Technology, Delft, The Netherlands, in 1977, 1979, and 1992, respectively. He joined Philips Research in 1979 to lead research projects in the area of video processing/analysis.
Model ‘Gerard de Haan’ Udo Klompenhouwer
Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of.
Gerard de Haan Visagie Hairstyling en Photography
Wenjin Wang, Bert den Brinker, Sander Stuijk, and Gerard de Haan Abstract—This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmogra-phy (rPPG).
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Gerard de Haan's 205 research works with 6,117 citations and 29,926 reads, including: Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection
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Authors Wenjin Wang , Albertus C Den Brinker , Gerard De Haan PMID: 30475707 DOI: 10.1109/TBME.2018.2882396 Abstract Camera-based remote photoplethysmography technology (remote-PPG) has shown great potential for contactless pulse-rate monitoring.
Gerard de Haan Visagie Hairstyling en Photography
Gerald de Haan has focused his entire scientific career on the identification of molecular mechanisms that control selfrenewal of hematopoietic stem cells. His early scientific contributions include papers in which the aging hematopoietic stem cell phenotype was described for the first time.
Gerard de Haan Visagie Hairstyling en Photography
Gerard de Haan. Eindhoven University of Technology. Verified email at tue.nl - Homepage. Video analysis Health monitoring. Articles Cited by Public access Co. MB Van Leeuwen, CHM Van Deurzen, G de Haan. Journal of microscopy 249 (2), 124-135, 2013. 133: 2013: IC for motion-compensated 100 Hz TV with natural-motion movie-mode. G De Haan, J.
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Gerard de Haan Abstract This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our.
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Gerard De Haan most often published in these fields: Artificial intelligence (100.00%) Computer vision (71.43%) Filter (signal processing) (57.14%) This overview was generated by a machine learning system which analysed the scientist's body of work. If you have any feedback, you.
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Robust Pulse Rate From Chrominance-Based rPPG. Gerard De Haan 1, Vincent Jeanne 1 • Institutions (1) 03 Jun 2013 - IEEE Transactions on Biomedical Engineering (IEEE) - Vol. 60, Iss: 10, pp 2878-2886. TL;DR: This work presents an analysis of the motion problem, from which far superior chrominance-based methods emerge, and shows remote.