W
Wanda Benesova
Researcher at Slovak University of Technology in Bratislava
Publications - 43
Citations - 483
Wanda Benesova is an academic researcher from Slovak University of Technology in Bratislava. The author has contributed to research in topics: Convolutional neural network & Image segmentation. The author has an hindex of 6, co-authored 40 publications receiving 383 citations. Previous affiliations of Wanda Benesova include Austrian Institute of Technology & Joanneum Research.
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Augmented reality to promote collaborative and autonomous learning in higher education
TL;DR: This work combines every learning process from the electrical machines course in the electrical engineering degree, which allows interactive and autonomous studying as well as collaborative performance of laboratory practices with other students and without a teacher's assistance.
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Eye blink detection based on motion vectors analysis
Andrej Fogelton,Wanda Benesova +1 more
TL;DR: The way how to evaluate eye blink detection algorithms without the impact of algorithms used for face and eye detection is extended and a new challenging dataset Researcher's night is introduced, which contains more than 100 unique individuals with 1849 annotated eye blinks.
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Exploring visual attention and saliency modeling for task-based visual analysis
TL;DR: Bottom-up saliency models tailored towards information visualization are not suitable for predicting visual attention when performing task-based visual analysis, and extensions to visual attention models are suggested to better account for task- based visual analysis.
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Eye blink completeness detection
Andrej Fogelton,Wanda Benesova +1 more
TL;DR: This work introduces the first method which detects blink completeness, and shows that using unidirectional RNN with time shifting achieves higher performance compared to a bidirectionals RNN, which is a suitable choice in this kind of problem where the feature pattern is not yet observed for the initial frames.
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Novelty-based Spatiotemporal Saliency Detection for Prediction of Gaze in Egocentric Video
TL;DR: A novel model for gaze prediction in egocentric video based on the spatiotemporal visual information captured from the wearer's camera is presented, specifically extended using a subjective function of surprise by means of motion memory, referring to the human aspect of visual attention.