Response Analysis of Eulerian Video Magnification

  • S. Ramya Marie
  • J. AnudevEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


The human eye has a very high optical resolution making it one of the most astonishing curiosities of the world. However, its spatial resolution is not good enough to capture everything happening around it, and it can miss out minor details, which can be termed as hidden movements. The hidden movements can be due to the extremely high speed of the visual, very small movements, long-term physical process, etc. Eulerian video magnification is a spatiotemporal video processing algorithm that can reveal hidden details that are otherwise hidden to naked eyes. In this process, a standard video sequence is spatially decomposed, and temporal filtering of the frames is done. The data so obtained as output can be used in many fields such as biomedical instrumentation, remote surveillance, etc. Here, an analysis has been done on Eulerian Video Magnification (EVM), for different video resolutions to understand its reliability.


Eulerian Video Magnification (EVM) Spatial resolution Motion magnification Spatiotemporal image processing 



The authors would like to express their gratitude to Dr. Ravikumar Pandi, Project Coordinator, Assistant Professor, Dept. of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri for his continuous support and motivation, and Chairperson Dr. Manjula G. Nair, Dept. of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri for providing all opportunities and facilities for the fulfilment of this work.

The authors also express their gratitude to the panel of reviewers who helped in reviewing the work and helped in organizing the contents.


  1. 1.
    Clarkvision photography [Online]. Available:
  2. 2.
  3. 3.
    Wadhwa N, Wu H-Y, Davis A, Rubinstein M, Shih E, Mysore GJ, Chen JG, Buyukozturk O, Guttag JV, Freeman WT et al (2016) Eulerian video magnification and analysis. Commun ACM 60(1):87–95CrossRefGoogle Scholar
  4. 4.
    Bennett SL, GoubranR, Knoefel F (2016) Adaptive Eulerian video magnification methods to extract heart rate from thermal video, pp 1–5Google Scholar
  5. 5.
    Menon HP, Narayanankutty KA (2016) MRI/CT image fusion using gabor texture features, vol 530, pp 47–60Google Scholar
  6. 6.
    Huang A, Xie L (2015) Healthinfo engineering: technology perspectives from evidence-based mhealth study in we-care project. Int J E-Health Med Commun 6(1):22–35CrossRefGoogle Scholar
  7. 7.
    Liu L, Stroulia E, Nikolaidis I, Miguel-Cruz A, Rincon AR (2016) Smart homes and home health monitoring technologies for older adults: a systematic review. Int J Med Inf 91:44–59CrossRefGoogle Scholar
  8. 8.
    Nilakant KR., Menon HP, Vikram K (2017) A survey on advanced segmentation techniques for brain MRI image segmentation. Int J Adv Sci Eng Inf Technol (Insight Society) 7(4):1448–1456CrossRefGoogle Scholar
  9. 9.
    Menon HP, Gayathri V (2017) Vasculature detection from retinal color fundus images using linear prediction residual algorithm. Int J Pure Appl Math (Academic Press) 114(12):171–178Google Scholar
  10. 10.
    Vadivelu S, Ganesan S, Murthy OR, Dhall A (2016) Thermal imaging based elderly fall detectionGoogle Scholar
  11. 11.
    Wu H-Y, Rubinstein M, Shih E, Guttag J, Durand F, Freeman W (2012) Eulerian video magnification for revealing subtle changes in the worldGoogle Scholar
  12. 12.
    Wadhwa N, Rubinstein M, Durand F, Freeman WT (2013) Phase-based video motion processing. ACM Trans Graph (Proceedings SIGGRAPH 2013) 32(4)CrossRefGoogle Scholar
  13. 13.
    Wadhwa N, Rubinstein M, Durand F, Freeman WT (2014) Riesz pyramids for fast phase-based video magnification. In: 2014 IEEE international conference on computational photography (ICCP). IEEE, pp 1–10Google Scholar
  14. 14.
    Liu C, Torralba A, Freeman WT, Durand F, Adelson EH (2005) Motion magnification. ACM Trans Graphics (TOG) 24(3):519–526CrossRefGoogle Scholar
  15. 15.
    Edwards DJ, Cattell M (1930) The action of compression on the contraction of heart muscle. Am J Physiol Legacy Content 93(1):90–96CrossRefGoogle Scholar
  16. 16.
    Brecelj T (2013) Eulerian video magnification. In: University of Ljubljana Faculty of Mathematics and Physics, pp 1–15Google Scholar
  17. 17.
    Instrumentation error calculation and setpoint determination. Engineering Standard ES-002, pp 1–14 (1994)Google Scholar
  18. 18.
    Rajevencelta J, Kumar CS, Cattell M (2016) Improving the performance of multi-parameter patient monitors using feature mapping and decision fusion. In: Region 10 conference (TENCON). IEEE, pp 1515–1518Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamKollamIndia

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