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Amr Hussein Yousef
Researcher at Old Dominion University
Publications - 24
Citations - 139
Amr Hussein Yousef is an academic researcher from Old Dominion University. The author has contributed to research in topics: Wiener filter & Lidar. The author has an hindex of 6, co-authored 22 publications receiving 110 citations. Previous affiliations of Amr Hussein Yousef include College of Business Administration & Alexandria University.
Papers
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Journal ArticleDOI
High-Speed Image Registration Algorithm with Subpixel Accuracy
TL;DR: A new, fast and computationally efficient lateral subpixel shift registration algorithm is presented that reduces computation time and memory requirements without sacricing the accuracy associated with the usual FFT approach accuracy.
Proceedings ArticleDOI
Movie success prediction using data mining
TL;DR: A mathematical model to predict the success and failure of the upcoming movies based on several attributes, including budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience is developed.
Proceedings ArticleDOI
Shoreline extraction from the fusion of LiDAR DEM data and aerial images using mutual information and genetic algrithms
TL;DR: A new algorithm is proposed that can effectively extract shorelines from fused LiDAR DEMs with aerial images depending on the availability of training data and offers better accuracy in shoreline extraction.
Journal ArticleDOI
Shoreline extraction from light detection and ranging digital elevation model data and aerial images
TL;DR: In this article, a multistep morphological technique was proposed to detect and eliminate the outliers that result from waves, etc., by means of an anomaly test with neighborhood constraints.
Proceedings ArticleDOI
On the restoration of the microscanned images captured from unmanned airborne vehicles
TL;DR: Based on the continuous-discrete-continuous (CDC) model, a Wiener restoration filter is used to restore the visually poor quality image to a super resolution (SR) image.