Martin Raubal
ETH Zurich
227 Papers
1.1K Citations
Martin Raubal is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Semantic similarity. The author has an hindex of 41, co-authored 191 publications. Previous affiliations of Martin Raubal include Vienna University of Technology & University of Maine.
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Papers
The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation
Andrew T. Duchowski,Krzysztof Krejtz,Izabela Krejtz,Cezary Biele,Anna Niedzielska,Peter Kiefer,Martin Raubal,Ioannis Giannopoulos +7 more
- 21 Apr 2018
TL;DR: A novel eye-tracked measure of the frequency of pupil diameter oscillation is proposed for capturing what is thought to be an indicator of cognitive load and is shown to discriminate task difficulty vis-a-vis cognitive load in an experiment.
199
A Formal Model of the Process of Wayfinding in Built Environments
Martin Raubal,Michael F. Worboys +1 more
- 25 Aug 1999
TL;DR: This paper presents a formal model of some aspects of the process of wayfinding, where appropriate elements of human perception and cognition are formally realized using image schemata and affordances using classical propositional logic.
The semantics of similarity in geographic information retrieval
TL;DR: This work introduces a framework to specify the semantics of similarity, and discusses similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geo- graphic information retrieval to demonstrate the applicability of the framework.
Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn
Rein Ahas,Anto Aasa,Yihong Yuan,Martin Raubal,Zbigniew Smoreda,Yu Liu,Cezary Ziemlicki,Margus Tiru,Matthew Zook +8 more
TL;DR: Methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’.
145
Where Am I? Investigating Map Matching During Self‐Localization With Mobile Eye Tracking in an Urban Environment
TL;DR: Observing the visual matching process between environment and map during self-localization with real-world mobile eye tracking shows that successful participants put significantly more visual attention to those symbols on the map that were helpful in the given situation than unsuccessful participants.