Analysis of periodicity in video sequences through dynamic linear modeling
Document Type
Conference Proceeding
Publication Date
1-1-2017
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10434 LNCS
First Page
386
Last Page
393
URL with Digital Object Identifier
10.1007/978-3-319-66185-8_44
Abstract
© Springer International Publishing AG 2017. 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.