Quantifying motion in video recordings of neonatal seizures by robust motion trackers based on block motion models.
This paper introduces a methodology for the development of robust motion trackers for video based on block motion models. According to this methodology, the motion of a site between two successive frames is estimated by minimizing an error function defined in terms of the intensities at these frames. The proposed methodology is used to develop robust motion trackers that rely on fractional block motion models. The motion trackers developed in this paper are utilized to extract motor activity signals from video recordings of neonatal seizures. The experimental results reveal that the proposed motion trackers are more accurate and reliable than existing motion tracking methods relying on pure translation and affine block motion models.
Department of Electrical and Computer Engineering, N308 Engineering Building 1, 4800 Calhoun Road, University of Houston, Houston, TX 77204-4005, USA. karayiannis@uh.edu
Nicolaos B
NB
Karayiannis
Yaohua
Y
Xiong
James D
JD
Frost
Merrill S
MS
Wise
Eli M
EM
Mizrahi
D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018730:Infant Behavior; D007231:Infant, Newborn; D008954:Models, Biological; D009043:Motor Activity; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*