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Aggelos K. Katsaggelos
Researcher at Northwestern University
Publications - 999
Citations - 28918
Aggelos K. Katsaggelos is an academic researcher from Northwestern University. The author has contributed to research in topics: Image restoration & Image processing. The author has an hindex of 76, co-authored 946 publications receiving 26196 citations. Previous affiliations of Aggelos K. Katsaggelos include University of Stavanger & Delft University of Technology.
Papers
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Journal ArticleDOI
The Prevalence of Psychotic Symptoms, Violent Ideation, and Disruptive Behavior in a Population With SARS-CoV-2 Infection: Preliminary Study
Sumra Bari,Nicole L. Vike,Khrystyna Stetsiv,Sean F. Woodward,Shamal Lalvani,Leandros Stefanopoulos,Byoung-Woo Kim,Nicos Maglaveras,Hans C. Breiter,Aggelos K. Katsaggelos +9 more
TL;DR: A preliminary study found that people who reported a test or clinician diagnosis of CO VID-19 also reported higher frequencies of violent ideation, disruptive behavior, or psychotic symptoms across multiple time windows, indicating that they were not likely to be the result of COVID-19.
Patent
Method, device and microprocessor for encoding/decoding a displaced frame difference in a motion compensated prediction-based video codec
TL;DR: In this paper, the authors proposed an iterative expansion of a displaced frame difference (DFD) image over a dictionary of modulated Gaussian functions for the purposes of video compression, which decomposes the DFD image into a set of coefficients which represent the perceptually important areas of a video frame.
Journal ArticleDOI
Detecting Screen Presence with Activity-Oriented RGB Camera in Egocentric Videos
Amit Adate,Soroush Shahi,Rawan Alharbi,Sougata Sen,Yang Gao,Aggelos K. Katsaggelos,Nabil Alshurafa +6 more
TL;DR: The potential for detecting screen-watching behavior in longitudinal studies using activity-oriented cameras is demonstrated, paving the way for a nuanced understanding of screen time’s relationship with health risk behaviors.
Journal ArticleDOI
Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification
TL;DR: Deep Gaussian Processes for Crowdsourcing (DGPCR) as mentioned in this paper was proposed to model the crowdsourcing problem with DGPs for the first time, and the behavior of each annotator is modeled with a confusion matrix among classes.