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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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Proceedings ArticleDOI
Tracking-optimal error control schemes for H.264 compressed video for vehicle surveillance
TL;DR: The proposed transportation video coding and transmission system is shown to give performance improvement over the current state-of-the-art system and yields bitrate savings of up to 60%.
Journal ArticleDOI
Integrated Dynamic 3D Imaging of Microbial Processes and Communities in Rhizosphere Environments: The Argonne Small Worlds Project
Kenneth M. Kemner,Mark Hereld,Norbert F. Scherer,Alan Selewa,Xiaolei Wang,Itay Gdor,Matthew K. Daddysman,Justin E. Jureller,Toan Huynh,Oliver Cossairt,Aggelos K. Katsaggelos,K. He,Seunghwan Yoo,N. Matsuda,Benjamin S. Glick,P. La Riviere,Jotham R. Austin,Kasey J. Day,Talon Chandler,S. Papanikou,Nicola J. Ferrier,D. Sholto-Douglas,Doga Gursoy,Olga Antipova,C. Soriano,Sarah L. O'Brien,Rosemarie Wilton,Angela J. Ahrendt,M. Asplund,Sarah Zerbs,Philippe Noirot,C. Atkins,Gyorgy Babnigg,Jessica L. Johnson,Shalaka Shinde,P. Korajczyk,M. F. Noirot +36 more
TL;DR: K. Kemner, M. Hereld, N. Scherer, A. Selewa, X. Wang, I. Shinde, P. Korajczyk, F. Noirot, C. Soriano, S. O’Brien, R. Wilton
Proceedings ArticleDOI
Bayesian logistic regression with sparse general representation prior for multispectral image classification
TL;DR: An algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns is developed.
Journal ArticleDOI
Compressive video sensing with limited measurements
TL;DR: A novel adaptive video compressive sensing algorithm to exploit the potential of CS in video acquisition by using the measurements made for the later frames to assist the recovery of previous ones, thus ensuring improved reconstruction quality even when the number of measurements for each frame is limited.
Proceedings ArticleDOI
Bayesian blind deconvolution from differently exposed image pairs
TL;DR: This paper presents a novel blind deconvolution algorithm for a pair of differently exposed images and employs a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates.