Amplification of low-amplitude object vibrations in videoframes
A.V. Zemskov, A.M. Gareev, D.P. Novikov

 

Mordovia Ogarev State University,

Samara State Aerospace University

Full text of article: Russian language.

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Abstract:
The paper proposes a variant of implementation of a filter for the amplification of the low-amplitude object vibrations in the video stream, describing the stages of the filter design. For this purpose, we conduct a mathematical analysis of the nature of vibrations and more complex movements of objects in the frames. The performance of the filter for movement amplification is tested, showing the feasibility of the non-contact determination of human respiration rate.

Keywords:
digital image filtering, spatial and temporal filtering, amplification of invisible vibrations in the frame, the Gabor filter, detection of low intensity useful signals in the video stream.

Citation:
Zemskov AV, Gareev AM, Novikov DP. Amplification of low-amplitude object vibrations in videoframes. Computer Optics 2015; 39(4): 606-13. DOI: 10.18287/0134-2452-2015-39-4-606-613.

References:

  1. Tarassenko L, Villarroel M, Guazzi A, Jorge J, Clifton D, Pugh C. Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiological Measurement 2014; 35: 807-31.
  2. Chen Y, Chang R, Jwo K, Hsu C, Tsao C. A Non-Contact Pulse Automatic Positioning Measurement System for Traditional Chinese Medicine. Sensors 2015; 15: 9899-914.
  3. Lewandowska M, Ruminski J, Kocejko T. Measuring Pulse Rate with a Webcam – a Non-contact Method for Evaluating Cardiac Activity. Proceedings of the Federated Conference on Computer Science and Information Systems 2014; 405-10.
  4. Sun Y, Hu S, Azorin-Peris V, Kalawsky R, Greenwald S. Noncontact imaging photoplethysmography to effectively access pulse rate variability. Journal of Biomedical Optics 2013; 18(6).
  5. Poh M, McDuff D, Picard R. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express 2010; 18(10): 10762-74.
  6. Hao-Yu Wu, Rubinstein M, Shih E, Guttag J, Durand F, Freeman W. Eulerian Video Magnification for Revealing Subtle Changes in the World. ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings. New York 2012; 31(65).
  7. Wadhwa N, Rubinstein M, Durand F, Freeman WT. Phase-based video motion processing. ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings. New York 2013; 32(80).
  8. Bradski G, Kaehler A. Learning OpenCV: Computer Vision with the OpenCV Library. Sebastopol: O'Reilly Media; 2008.
  9. Priorov AL, Apalkov IV, Hrjashhev VV. Digital image processing [In Russian]. Jaroslavl; 2007.
  10. Dayan P, Abbot LF. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge: MIT Press; 2001.
  11. Florczyk S. Robot Vision: Video-based Indoor Exploration with Autonomous and Mobile Robots. Weinheim: Willey-VCH; 2005.
  12. Kong A. An evaluation of Gabor orientation as a feature for face recognition. Pattern Recognition 2008.
  13. Spicyn VG, Kermani Kolankeh A, Hamker F. Finding settings and delete the constant component of the Gabor filter for image processing [In Russian]. The Tomsk Polytechnic University 2011; 318(5): 57-9.
  14. Gonzalez RC, Woods RE, Eddins SL. Digital Image Processing Using MATLAB. Gatesmark Publishing; 2009.
  15. Pratt WK. Digital Image Processing. Los Altos, California: PixelSoft; 1982.
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