A
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
More filters
Proceedings ArticleDOI
Geospatial image mining for nuclear proliferation detection: Challenges and new opportunities
Ranga Raju Vatsavai,Budhendra L. Bhaduri,Anil Cheriyadat,Lloyd F. Arrowood,Eddie A Bright,Shaun S. Gleason,Carl F. Diegert,Aggelos K. Katsaggelos,Thrasos Pappas,Reid B. Porter,James S. Bollinger,Barry Chen,Ryan E. Hohimer +12 more
TL;DR: The current understanding of geospatial image mining techniques is described and key gaps are enumerated and future research needs in the context of nuclear proliferation are identified.
Proceedings ArticleDOI
Iterative deconvolution using several different distorted versions of an unknown signal
TL;DR: This paper analyses the error behavior of iterative deconvolution algorithms when the distorting system has a frequency response that has negative real part or has a finite number of isolated zeros and suggests a new algorithm that incorporates multiple distorted versions of the signal.
Book ChapterDOI
Audio-Visual and Visual-Only Speech and Speaker Recognition: Issues about Theory, System Design, and Implementation
Proceedings ArticleDOI
A compressed video enhancement algorithm
TL;DR: The problem of the enhancement of a low bit-rate compressed video sequence using the information provided by the encoder using a spatio-temporally adaptive algorithm that enforces different degrees of between-block, within- block, and temporal smoothness of the decompressed frames based on macroblock types is investigated.
Journal ArticleDOI
Fast video shot retrieval based on trace geometry matching
TL;DR: In this article, a fast video shot retrieval algorithm based on the geometry of video sequence traces in the principal component space is presented, in which techniques to address scale (spatial and temporal) issues, in addition to noise and other possible distortions, such as frame dropping, are discussed.