Abdul Sami

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This paper presents an automated procedure developed to extract quantitative information from video recordings of neonatal seizures in the form of motor activity signals. This procedure relies on optical flow computation to select anatomical sites located on the infants' body parts. Motor activity signals are extracted by tracking selected anatomical sites(More)
PURPOSE The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. The long-term goal of this research is the integration of these computational procedures into the development of a stand-alone automated system that could be(More)
This work introduces predictive block matching, a method developed to track motion in video by exploiting the advantages of block motion estimation and adaptive block matching. The proposed method relies on a pure translation motion model to estimate the displacement of a block between two successive video frames before initiating the search for the best(More)
relatively expensive, are generally used for only a few hours of monitoring, and may not be routinely available in many centers. Automated processing and analysis of video recordings of neonatal seizures can generate novel methods for extracting quantitative information that is relevant only to the seizure. The extraction of quantitative information from(More)
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