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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
Lip tracking for MPEG-4 facial animation
TL;DR: Based on the results of the outer lip tracking, the inner lip is tracked using a similarity function and a temporal smoothness constraint and a novel method consisting of a Gradient Vector Flow snake with a parabolic template as an additional external force is proposed.
Posted Content
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
TL;DR: DeepBinaryMask as mentioned in this paper is an encoder-decoder neural network model for video compressive sensing, where the encoder learns the binary elements of the sensing matrix and the decoder is trained to recover the unknown video sequence.
Proceedings ArticleDOI
Toward a new video compression scheme using super-resolution
TL;DR: This paper considers the following three video compression models and describes the application of super-resolution techniques as a way to post-process and upsample a compressed video sequences.
Journal ArticleDOI
Bayesian K-SVD Using Fast Variational Inference
TL;DR: A fully-automated Bayesian method is proposed that considers the uncertainty of the estimates and produces a sparse representation of the data without prior information on the number of non-zeros in each representation vector and develops an efficient variational inference framework that reduces computational complexity.
Journal ArticleDOI
Iterative restoration of fast-moving objects in dynamic image sequences
TL;DR: In this article, the authors consider the iterative restoration of images blurred by distinct, fast moving objects in the frames of a (video) image sequence and propose a robust iterative approach which allows for the incorporation of prior knowledge of the scene structure into the algorithm to facilitate the restoration of difficult scenes.