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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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A survey of classical methods and new trends in pansharpening of multispectral images
TL;DR: A review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics and how the quality of the pansharpened images can be assessed both visually and quantitatively is analyzed.
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Sparse Bayesian Methods for Low-Rank Matrix Estimation
TL;DR: In this paper, a matrix factorization formulation and enforcing the low-rank constraint in the estimates as a sparsity constraint are used to determine the correct rank while providing high recovery performance.
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Automatic facial expression recognition using facial animation parameters and multistream HMMs
TL;DR: The proposed multistream HMM facial expression system, which utilizes stream reliability weights, achieves relative reduction of the facial expression recognition error of 44% compared to the single-stream HMM system.
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Variational Bayesian Blind Deconvolution Using a Total Variation Prior
TL;DR: Novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework using a hierarchical Bayesian model to provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.
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Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
TL;DR: It is shown how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations.