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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.
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Book
Digital Image Restoration
TL;DR: The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the readers who may already be well-versed in image restoration.
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Hybrid image segmentation using watersheds and fast region merging
TL;DR: A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds and additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced.
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Bayesian Compressive Sensing Using Laplace Priors
TL;DR: This paper model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework and develops a constructive (greedy) algorithm designed for fast reconstruction useful in practical settings.
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Error resilient video coding techniques
TL;DR: The majority of the article is devoted to the techniques developed for block-based hybrid coders using motion-compensated prediction and transform coding, and a separate section covers error resilience techniques for shape coding in MPEG-4.
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Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation
TL;DR: An error analysis based on an objective mean-square-error (MSE) criterion is used to motivate regularization and two approaches for choosing the regularization parameter and estimating the noise variance are proposed.