Analysis of Periodicity in Video Sequences Through Dynamic Linear Modeling

  • A. Jonathan McLeodEmail author
  • Dante P. I. Capaldi
  • John S. H. Baxter
  • Grace Parraga
  • Xiongbiao Luo
  • Terry M. Peters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


Periodicity is an important characteristic in many types of video sequences, particularly in medical applications where the cardiac and respiratory cycles are of special significance. Simple spectral analysis or band-pass filtering is often insufficient to extract the periodic signal. Here, we propose modeling the periodic and background components using nested dynamic linear models. These models can approximate the periodic and background time series in a wide range of video sequences. A likelihood ratio test can be used to find regions of the video exhibiting periodicity. Our experiments suggested this technique is suitable for a variety of applications using different imaging modalities, including ultrasound, MRI and natural video.


Periodicity detection Dynamic linear models Remote photoplethysmogram Perfusion mapping Fourier-decomposition MRI 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • A. Jonathan McLeod
    • 1
    • 2
    Email author
  • Dante P. I. Capaldi
    • 1
  • John S. H. Baxter
    • 1
    • 2
  • Grace Parraga
    • 1
  • Xiongbiao Luo
    • 3
  • Terry M. Peters
    • 1
    • 2
  1. 1.Robarts Research InstituteWestern UniversityLondonCanada
  2. 2.Biomedical Engineering Graduate ProgramWestern UniversityLondonCanada
  3. 3.Department of Computer ScienceXiamen UniversityXiamenChina

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