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Konstantinos N. Plataniotis
Researcher at University of Toronto
Publications - 641
Citations - 19908
Konstantinos N. Plataniotis is an academic researcher from University of Toronto. The author has contributed to research in topics: Color image & Computer science. The author has an hindex of 63, co-authored 595 publications receiving 16695 citations. Previous affiliations of Konstantinos N. Plataniotis include Florida Institute of Technology & University of Cambridge.
Papers
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Book
Color Image Processing and Applications
TL;DR: In this article, the authors present an introductory chapter on colour, followed by four chapters on image processing, or omit them and move directly to the final three chapters that deal with colour image analysis and coding.
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MPCA: Multilinear Principal Component Analysis of Tensor Objects
TL;DR: It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.
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Face recognition using kernel direct discriminant analysis algorithms
TL;DR: This paper proposes a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution and effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks.
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COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images.
Parnian Afshar,Shahin Heidarian,Farnoosh Naderkhani,Anastasia Oikonomou,Konstantinos N. Plataniotis,Arash Mohammadi +5 more
TL;DR: Results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of CO VID-19.
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Kernel-Based Positioning in Wireless Local Area Networks
TL;DR: It is shown that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results and a kernelized distance calculation algorithm for comparing RSS observations to RSS training records is presented.