What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Human–computer interaction
His primary areas of study are Artificial intelligence, Computer vision, Eye tracking, Gaze and Eye movement.
His is doing research in Activity recognition and Gesture, both of which are found in Artificial intelligence.
The concepts of his Activity recognition study are interwoven with issues in Multimedia and Inertial measurement unit.
Andreas Bulling works mostly in the field of Computer vision, limiting it down to topics relating to Data set and, in certain cases, Motion, as a part of the same area of interest.
He combines subjects such as Visual angle, Image processing, Ubiquitous computing, Human–computer interaction and Set with his study of Eye tracking.
His work deals with themes such as Appearance based, Modality, Usability, Convolutional neural network and Rendering, which intersect with Gaze.
His most cited work include:
- A tutorial on human activity recognition using body-worn inertial sensors (863 citations)
- Eye Movement Analysis for Activity Recognition Using Electrooculography (422 citations)
- Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction (342 citations)
What are the main themes of his work throughout his whole career to date?
Andreas Bulling mainly investigates Artificial intelligence, Gaze, Computer vision, Eye tracking and Human–computer interaction.
Andreas Bulling usually deals with Artificial intelligence and limits it to topics linked to Machine learning and Visual search.
His biological study spans a wide range of topics, including Mobile device, Public displays, BitTorrent tracker and Usability.
His work in the fields of Rendering, Face and Monocular overlaps with other areas such as Pupil.
He has included themes like Calibration, Modality, Computer graphics and Augmented reality in his Eye tracking study.
His research integrates issues of Multimedia, Key and Gesture in his study of Human–computer interaction.
He most often published in these fields:
- Artificial intelligence (58.87%)
- Gaze (57.58%)
- Computer vision (48.05%)
What were the highlights of his more recent work (between 2018-2021)?
- Gaze (57.58%)
- Artificial intelligence (58.87%)
- Eye tracking (45.89%)
In recent papers he was focusing on the following fields of study:
Gaze, Artificial intelligence, Eye tracking, Computer vision and Human–computer interaction are his primary areas of study.
His Gaze study combines topics in areas such as User modeling, Vergence, Usability and Visual search.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His Eye tracking research focuses on Augmented reality and how it relates to Head.
His research investigates the link between Computer vision and topics such as Calibration that cross with problems in Exploit.
His Human–computer interaction study integrates concerns from other disciplines, such as Visualization, Key, Mobile device and Rendering.
Between 2018 and 2021, his most popular works were:
- MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (115 citations)
- A Design Space for Gaze Interaction on Head-mounted Displays (26 citations)
- PrivacEye: privacy-preserving head-mounted eye tracking using egocentric scene image and eye movement features (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Human–computer interaction
His scientific interests lie mostly in Artificial intelligence, Gaze, Eye tracking, Computer vision and Human–computer interaction.
His work on Convolutional neural network, Deep learning and Paraphrase is typically connected to SIGNAL as part of general Artificial intelligence study, connecting several disciplines of science.
His work in Gaze covers topics such as User modeling which are related to areas like Vergence.
Andreas Bulling interconnects Augmented reality, Eye movement and Head in the investigation of issues within Eye tracking.
His Eye movement research is multidisciplinary, relying on both Computing Methodologies and Data set.
His work on Appearance based as part of general Computer vision research is frequently linked to Pupil, Shutter and Gauge, bridging the gap between disciplines.
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