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Mikhail Startsev
Mikhail Startsev
Senior AI Software Developer, Snke OS GmbH
Verified email at tum.de - Homepage
Title
Cited by
Cited by
Year
1D CNN with BLSTM for automated classification of fixations, saccades, and smooth pursuits
M Startsev, I Agtzidis, M Dorr
Behavior Research Methods 51, 556-572, 2019
972019
360-aware saliency estimation with conventional image saliency predictors
M Startsev, M Dorr
Signal Processing: Image Communication 69, 43-52, 2018
672018
360-degree video gaze behaviour: A ground-truth data set and a classification algorithm for eye movements
I Agtzidis, M Startsev, M Dorr
Proceedings of the 27th ACM international conference on multimedia, 1007-1015, 2019
492019
Classifying autism spectrum disorder based on scanpaths and saliency
M Startsev, M Dorr
2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW …, 2019
312019
Smooth pursuit detection based on multiple observers
I Agtzidis, M Startsev, M Dorr
Proceedings of the ninth biennial acm symposium on eye tracking research …, 2016
272016
In the pursuit of (ground) truth: A hand-labelling tool for eye movements recorded during dynamic scene viewing
I Agtzidis, M Startsev, M Dorr
2016 IEEE second workshop on eye tracking and visualization (ETVIS), 65-68, 2016
222016
Free visual exploration of natural movies in schizophrenia
JE Silberg, I Agtzidis, M Startsev, T Fasshauer, K Silling, A Sprenger, ...
European archives of psychiatry and clinical neuroscience 269, 407-418, 2019
202019
Evaluating eye movement event detection: A review of the state of the art
M Startsev, R Zemblys
Behavior Research Methods 55 (4), 1653-1714, 2023
192023
Characterizing and automatically detecting smooth pursuit in a large-scale ground-truth data set of dynamic natural scenes
M Startsev, I Agtzidis, M Dorr
Journal of Vision 19 (14), 10-10, 2019
162019
Smooth pursuit
M Startsev, I Agtzidis, M Dorr
オンライン]. Available: http://michaeldorr. de/smoothpursuit/.[アクセス日: 28 1 2020], 2016
152016
Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching
I Agtzidis, M Startsev, M Dorr
Journal of Eye Movement Research 13 (4), 2020
82020
A ground-truth data set and a classification algorithm for eye movements in 360-degree videos
I Agtzidis, M Startsev, M Dorr
arXiv preprint arXiv:1903.06474, 2019
82019
Supersaliency: a novel pipeline for predicting smooth pursuit-based attention improves generalisability of video saliency
M Startsev, M Dorr
IEEE Access 8, 1276-1289, 2019
52019
Manual & automatic detection of smooth pursuit in dynamic natural scenes
M Startsev, I Agtzidis, M Dorr
Proceedings of the European conference of eye movements, 2017
52017
A novel gaze event detection metric that is not fooled by gaze-independent baselines
M Startsev, S Göb, M Dorr
Proceedings of the 11th ACM symposium on eye tracking research …, 2019
42019
Increasing video saliency model generalizability by training for smooth pursuit prediction
M Startsev, M Dorr
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
42018
Sequence-to-sequence deep learning for eye movement classification
M Startsev, I Agtzidis, M Dorr
Perception 48, 200-200, 2019
32019
Improving the state of the art in eye movement event detection via trainable label correction.
M Startsev, M Dorr
Journal of Eye Movement Research 12 (7), 2019
12019
Deep learning vs. manual annotation of eye movements
M Startsev, I Agtzidis, M Dorr
Proceedings of the 2018 ACM Symposium on Eye Tracking Research …, 2018
12018
Supersaliency: Predicting Smooth Pursuit-Based Attention with Slicing CNNs Improves Fixation Prediction for Naturalistic Videos
M Startsev, M Dorr
12018
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