I’m a Phd student in the Perceptual User Interfaces group since December 2014.
contact
mail:
phone: +49 (0)681 9325 2132
…or just drop by at E1.4, room 622.
education
I hold a Bachelor’s degree in Computer Science from Saarland University in February 2013. Instead of continuing the Master’s program in Computer Science I specialized in the fields of image acquisition, analysis and synthesis requiring profound scientific knowledge, in particular in computer science, mathematics, physics, engineering, and cognitive science. In December 2014 I received an interdisciplinary Master’s degree in Visual Computing from Saarland University.
research interests
Mobile Eye Tracking
Image Processing and Computer Vision
Machine Learning and Pattern Recognition
Human-Computer Interaction
selected publications
Mohsen Mansouryar; Julian Steil; Yusuke Sugano; Andreas Bulling
@inproceedings{mansouryar16_etra,
title = {3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers},
author = {Mohsen Mansouryar and Julian Steil and Yusuke Sugano and Andreas Bulling},
url = {https://github.molgen.mpg.de/perceptual/etra2016_gazesim
https://perceptual.mpi-inf.mpg.de/files/2016/01/mansouryar16_etra.pdf},
doi = {10.1145/2857491.2857530},
year = {2016},
date = {2016-01-01},
booktitle = {Proc. of the 9th ACM International Symposium on Eye Tracking Research & Applications (ETRA 2016)},
pages = {197-200},
abstract = {3D gaze information is important for scene-centric attention analysis, but accurate estimation and analysis of 3D gaze in real-world environments remains challenging. We present a novel 3D gaze estimation method for monocular head-mounted eye trackers. In contrast to previous work, our method does not aim to infer 3D eye- ball poses, but directly maps 2D pupil positions to 3D gaze directions in scene camera coordinate space. We first provide a detailed discussion of the 3D gaze estimation task and summarize different methods, including our own. We then evaluate the performance of different 3D gaze estimation approaches using both simulated and real data. Through experimental validation, we demonstrate the effectiveness of our method in reducing parallax error, and we identify research challenges for the design of 3D calibration procedures.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
3D gaze information is important for scene-centric attention analysis, but accurate estimation and analysis of 3D gaze in real-world environments remains challenging. We present a novel 3D gaze estimation method for monocular head-mounted eye trackers. In contrast to previous work, our method does not aim to infer 3D eye- ball poses, but directly maps 2D pupil positions to 3D gaze directions in scene camera coordinate space. We first provide a detailed discussion of the 3D gaze estimation task and summarize different methods, including our own. We then evaluate the performance of different 3D gaze estimation approaches using both simulated and real data. Through experimental validation, we demonstrate the effectiveness of our method in reducing parallax error, and we identify research challenges for the design of 3D calibration procedures.
@inproceedings{Steil_Ubicomp15,
title = {Discovery of Everyday Human Activities From Long-Term Visual Behaviour Using Topic Models},
author = {Julian Steil and Andreas Bulling},
url = {https://perceptual.mpi-inf.mpg.de/files/2015/08/Steil_Ubicomp15.pdf},
doi = {10.1145/2750858.2807520},
year = {2015},
date = {2015-05-21},
booktitle = {Proc. of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)},
pages = {75-85},
abstract = {Human visual behaviour has significant potential for activity recognition and computational behaviour analysis, but previous works focused on supervised methods and recognition of predefined activity classes based on short-term eye movement recordings. We propose a fully unsupervised method to discover users' everyday activities from their long-term visual behaviour. Our method combines a bag-of-words representation of visual behaviour that encodes saccades, fixations, and blinks with a latent Dirichlet allocation (LDA) topic model.
We further propose different methods to encode saccades for their use in the topic model. We evaluate our method on a novel long-term gaze dataset that contains full-day recordings of natural visual behaviour
of 10 participants (more than 80 hours in total). We also provide annotations for eight sample activity classes (outdoor, social interaction, focused work, travel, reading, computer work, watching media, eating) and periods with no specific activity. We show the ability of our method to discover these activities with performance competitive with that of previously published supervised methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Human visual behaviour has significant potential for activity recognition and computational behaviour analysis, but previous works focused on supervised methods and recognition of predefined activity classes based on short-term eye movement recordings. We propose a fully unsupervised method to discover users' everyday activities from their long-term visual behaviour. Our method combines a bag-of-words representation of visual behaviour that encodes saccades, fixations, and blinks with a latent Dirichlet allocation (LDA) topic model.
We further propose different methods to encode saccades for their use in the topic model. We evaluate our method on a novel long-term gaze dataset that contains full-day recordings of natural visual behaviour
of 10 participants (more than 80 hours in total). We also provide annotations for eight sample activity classes (outdoor, social interaction, focused work, travel, reading, computer work, watching media, eating) and periods with no specific activity. We show the ability of our method to discover these activities with performance competitive with that of previously published supervised methods.
