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Practical Issues For Real-Time Video Tracking
2012
Zenodo
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Tayanov, Ph.D. is Researcher in Polish-Japanese Institute of Information Technology (PJIIT), Bytom, 41-902, Poland e-mail:vtayanov@yahoo.com. an object. ...
doi:10.5281/zenodo.1077295
fatcat:5wb3cyltxna6bhcgr2okcieutm
New Principles in Algorithm Design for Problems of Face Recognition
[chapter]
2011
Reviews, Refinements and New Ideas in Face Recognition
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Reviews, Refinements and New Ideas in Face Recognition
How to reference In order to correctly reference this scholarly work, feel free to copy and paste the following: Vitaliy Tayanov (2011) . ...
This approach has been developed for metrical classifiers and classifiers on the basis of distance function in Tayanov & Lutsyk, 2009 ]. ...
doi:10.5772/18391
fatcat:tkftt7gqcne55dgaqvyfgy5z5i
Super-resolution pipeline for fast adjudication in watchlist screening
2015
2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)
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Although still-to-video face recognition is an important function in watchlist screening, state-of-the-art systems often yield limited performance due to camera inter-operability and to variations in capture conditions. Therefore, the visual comparison of faces captured in unconstrained low-quality videos against a matching high-quality reference facial still image captured under controlled conditions is required in many surveillance applications to limit the number of costly false matches. To
doi:10.1109/ipta.2015.7367145
dblp:conf/ipta/TayanovGLH15
fatcat:xlu7xssggvaxzgatg7pbxjbjti
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... mprove the visual appearance of faces captured in videos, this paper presents a new super-resolution (SR) pipeline that is suitable for fast adjudication of face-matches produced by an automated system. In this pipeline, face quality measures are used to rank and select face captures belonging to a facial trajectory, and multi-image SR iteratively enhances the appearance of a super-resolved face image. Face selection is optimized and registered using graphical models. Experiments with the Chokepoint dataset show that the proposed pipeline efficiently produces super-resolved face images by ranking best quality ROIs in a trajectory. To select the best face captures for SR, this pipeline exploits a strong correlation existing between pose and sharpness quality measurements.
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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DAY 4 -Jan 15, 2021
Roth, Wolfgang; Schindler,
Günther; Fröning, Holger;
Pernkopf, Franz
2862
On Resource-efficient Bayesian Network Classifiers and Deep
Neural Networks
DAY 4 -Jan 15, 2021
Tayanov ...
, Vitaliy; Krzyzak, Adam;
Suen, Ching Y
2879
Comparison of Stacking-based Classifier Ensembles using Euclidean
and Riemannian Geometries
DAY 4 -Jan 15, 2021
Mallick, Tanwi; Balaprakash,
Prasanna ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm