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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Catching your eyes: AI-driven modeling and analysis of eye-tracking data (ETS)- Dozent/in
- Dr. Dario Zanca
- Angaben
- Seminar
Präsenz
2 SWS, ECTS-Studium, ECTS-Credits: 2,5
für FAU Scientia Gaststudierende zugelassen, Sprache Englisch
Zeit und Ort: Di 12:15 - 13:45, 00.010; Bemerkung zu Zeit und Ort: Organisation and slides via StudOn.
- Voraussetzungen / Organisatorisches
- Assignment: mailto: dario.zanca@fau.de
The grade is based on a presentation and a report, both counting 50% of the final grade.
- Inhalt
- Learning objectives:
Be familiar with an eye-tracking experimental setup and eye-tracking data.
Knowledge of the common eye-tracking data analysis techniques.
Knowledge of the state-of-the-art saliency and scanpath models to predict human visual attention.
Contents
Seeing is a complex activity. Humans perform eye movements to actively seek for useful information, while regulating pupil size to control the amount of light to be captured. Eye-tracking can be used to record the eye’s activity. It is a powerful tool to study human gaze behavior and it can be used to assess the health condition of individuals.
The aim of this seminar is to become familiar with eye-tracking data and their use in different domains, from neuroscience and artificial intelligence (to understand and simulate human attention), to medicine and psychology (to identify eye-tracking based biomarkers).
Different methods will be introduced and compared. Students will study on state-of-the-art papers and present the details of the chosen topic described in the papers. Alternatively, the student may work on experimental task and present the result of applying state of the art methods.
- Empfohlene Literatur
- • Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence, 20(11), 1254-1259.
• Borji, A., & Itti, L. (2012). State-of-the-art in visual attention modeling. IEEE transactions on pattern analysis and machine intelligence, 35(1), 185-207.
• Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009, September). Learning to predict where humans look. In 2009 IEEE 12th international conference on computer vision (pp. 2106-2113). IEEE.
• Zanca, D., & Gori, M. (2017, December). Variational laws of visual attention for dynamic scenes. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 3826-3835).
• Zanca, D., Melacci, S., & Gori, M. (2019). Gravitational laws of focus of attention. IEEE transactions on pattern analysis and machine intelligence, 42(12), 2983-2995.
• Zanca, D., Gori, M., Melacci, S., & Rufa, A. (2020). Gravitational models explain shifts on human visual attention. Scientific Reports, 10(1), 1-9.
• Bellet, M. E., Bellet, J., Nienborg, H., Hafed, Z. M., & Berens, P. (2019). Human-level saccade detection performance using deep neural networks. Journal of neurophysiology, 121(2), 646-661.
• Piu, P., Serchi, V., Rosini, F., & Rufa, A. (2019). A cross-recurrence analysis of the pupil size fluctuations in steady scotopic conditions. Frontiers in neuroscience, 13, 407.
• Zénon, A. (2017). Time-domain analysis for extracting fast-paced pupil responses. Scientific reports, 7(1), 1-10.
• Bargagli, A., Fontanelli, E., Zanca, D., Castelli, I., Rosini, F., Maddii, S., ... & Rufa, A. (2020). Neurophthalmologic and Orthoptic Ambulatory Assessments Reveal Ocular and Visual Changes in Patients With Early Alzheimer and Parkinson's Disease. Frontiers in Neurology, 11.
- ECTS-Informationen:
- Credits: 2,5
- Zusätzliche Informationen
- Schlagwörter: eye-tracking, human visual attention, biomarkers, artificial intelligence
Erwartete Teilnehmerzahl: 15, Maximale Teilnehmerzahl: 20
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2022:
- Catching your eyes: AI-driven modeling and analysis of eye-tracking data (ETS)
- Institution: Lehrstuhl für Maschinelles Lernen und Datenanalytik
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