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ORCIDMikhail Startsev
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Books and Theses
- 2020
[b1]Mikhail Startsev:
Computationally Modelling Human Visual Perception: Eye Movements and Saliency. Technical University of Munich, Germany, 2020
Journal Articles
- 2020
- [j2]Mikhail Startsev
, Michael Dorr:
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalisability of Video Saliency. IEEE Access 8: 1276-1289 (2020) - 2018
- [j1]Mikhail Startsev, Michael Dorr:
360-aware saliency estimation with conventional image saliency predictors. Signal Process. Image Commun. 69: 43-52 (2018)
Conference and Workshop Papers
- 2019
- [c7]Mikhail Startsev, Stefan Göb, Michael Dorr:
A novel gaze event detection metric that is not fooled by gaze-independent baselines. ETRA 2019: 22:1-22:9 - [c6]Mikhail Startsev, Michael Dorr:
Classifying Autism Spectrum Disorder Based on Scanpaths and Saliency. ICME Workshops 2019: 633-636 - [c5]Ioannis Agtzidis, Mikhail Startsev, Michael Dorr:
360-degree Video Gaze Behaviour: A Ground-Truth Data Set and a Classification Algorithm for Eye Movements. ACM Multimedia 2019: 1007-1015 - 2018
- [c4]Mikhail Startsev, Michael Dorr:
Increasing Video Saliency Model Generalizability by Training for Smooth Pursuit Prediction. CVPR Workshops 2018: 1969-1972 - [c3]Mikhail Startsev, Ioannis Agtzidis, Michael Dorr:
Deep learning vs. manual annotation of eye movements. ETRA 2018: 101:1-101:3 - 2016
- [c2]Ioannis Agtzidis, Mikhail Startsev, Michael Dorr:
Smooth pursuit detection based on multiple observers. ETRA 2016: 303-306 - [c1]Ioannis Agtzidis, Mikhail Startsev, Michael Dorr:
In the pursuit of (ground) truth: a hand-labelling tool for eye movements recorded during dynamic scene viewing. ETVIS 2016: 65-68
Informal and Other Publications
- 2019
- [i2]Ioannis Agtzidis, Mikhail Startsev, Michael Dorr:
A Ground-Truth Data Set and a Classification Algorithm for Eye Movements in 360-degree Videos. CoRR abs/1903.06474 (2019) - 2018
- [i1]Mikhail Startsev, Michael Dorr:
Supersaliency: Predicting Smooth Pursuit-Based Attention with Slicing CNNs Improves Fixation Prediction for Naturalistic Videos. CoRR abs/1801.08925 (2018)
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