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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
Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.
Jinhyeong Bae,Jane Stocks,Ashley Heywood,Youngmoon Jung,Lisanne M. Jenkins,Virginia Hill,Aggelos K. Katsaggelos,Karteek Popuri,Howie Rosen,M. Faisal Beg,Lei Wang,Alzheimer’s Disease Neuroimaging Initiative +11 more
TL;DR: A deep learning model to predict conversion from MCI to DAT, which resulted in 82.4% classification accuracy at the target task, outperforming current models in the field and showing that the model is able to predict an individual patient's future cognitive decline.
Journal ArticleDOI
Bayesian Blind Deconvolution From Differently Exposed Image Pairs
TL;DR: This paper addresses the problem of utilizing two such images in order to obtain an estimate of the original scene and presents a novel blind deconvolution algorithm for solving it in a hierarchical Bayesian framework.
Book ChapterDOI
Reconstruction of a High Resolution Image from Multiple Low Resolution Images
TL;DR: In this chapter the problem of reconstructing a high resolution image from multiple aliased and shifted by sub-pixel shifts low resolution images is considered and two approaches for solving this low-to-high resolution problem are presented.
Journal ArticleDOI
Shape error concealment using Hermite splines
TL;DR: This paper proposes a post-processing shape error-concealment technique that uses geometric boundary information of the received /spl alpha/-plane and third order Hermite splines to model the missing boundary.
Journal ArticleDOI
Low-Complexity Tracking-Aware H.264 Video Compression for Transportation Surveillance
TL;DR: A tracking-aware, H.264-compliant compression algorithm that removes temporal components of low tracking interest and optimizes the quantization of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy is proposed.