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Andreas Koschan
Researcher at University of Tennessee
Publications - 176
Citations - 4243
Andreas Koschan is an academic researcher from University of Tennessee. The author has contributed to research in topics: Video tracking & Facial recognition system. The author has an hindex of 31, co-authored 176 publications receiving 4087 citations. Previous affiliations of Andreas Koschan include Free University of Berlin & Technical University of Berlin.
Papers
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Book
Computer Vision: Three-Dimensional Data from Images
TL;DR: This computer vision textbook describes the reconstruction of object surfaces and the analysis of distances between camera and objects, main topics are static and dynamic stereo analysis, shape from shading, photometric stereoAnalysis, and structured lighting.
Journal ArticleDOI
Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition
Seong G. Kong,Jingu Heo,Faysal Boughorbel,Yue Zheng,Besma Abidi,Andreas Koschan,Mingzhong Yi,Mongi A. Abidi +7 more
TL;DR: In this paper, an ellipse fitting method was used to detect eyeglass regions and replaced with eye template patterns to preserve the details useful for face recognition in the fused image.
Journal ArticleDOI
Detection and classification of edges in color images
Andreas Koschan,Mongi A. Abidi +1 more
TL;DR: Various vector-valued techniques for detecting discontinuities in color images are discussed, mainly based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection.
Proceedings ArticleDOI
Perception-based 3D triangle mesh segmentation using fast marching watersheds
TL;DR: An algorithm called fast marching watersheds that segments a triangle mesh into visual parts that leverages a human vision theory known as the minima rule to identify regions bounded by contours of negative curvature minima.
Book
Digital Color Image Processing
Andreas Koschan,Mongi A. Abidi +1 more
TL;DR: Digital Color Image Processing is the only book that covers the breadth of the subject under one convenient cover and is written at a level that is accessible for first- and second-year graduate students in electrical and computer engineering and computer science courses, and that is appropriate for researchers who wish to extend their knowledge in the area of color image processing.