Lynn Schmittwilken
Photo: SCIoI
Lynn Schmittwilken obtained her B.Sc in Psychology and her M.Sc. Neurosciences at Bremen University with a main focus on mammal brain and mammal behavior with a main interest in the human brain. She received insights into the complexity and functionality of the central nervous system through her specialization in visual perception, which she researched using methods such as electrophysiology, psychophysics as well as computational modeling. As a SCIoI doctoral student, Lynn researched study visual processing in humans. Her interests lie in how humans synthesize the three-dimensional nature of our surrounds, and how this information could be used to improve computer vision models.
Click here to see Lynn’s final dissertation.
Projects
Lynn Schmittwilken is member of:
Publications +
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Schmittwilken
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20011
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Schmittwilken, L. (2026). Seeing by moving: revisiting pattern vision through fixational eye movements [Technische Universität Berlin]. https://doi.org/10.14279/DEPOSITONCE-25108
Schmittwilken, L., Wichmann, F. A., & Maertens, M. (2024). Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies. Vision Research, 222, 108450. https://doi.org/10.1016/j.visres.2024.108450
Schmittwilken, L., & Maertens, M. (2022). Fixational eye movements enable robust edge detection. Journal of Vision, 22(8), 5. https://doi.org/10.1167/jov.22.8.5
Schmittwilken, L., & Maertens, M. (2022, May). Medium spatial frequencies mask edges most effectively. Vision Sciences Society Annual Meeting (VSS). https://doi.org/10.1167/jov.22.14.4041
Schmittwilken, L., Matic, M., Maertens, M., & Vincent, J. (2022). Contributed Session III: BRENCH: An open-source framework for b(r)enchmarking brightness models. Journal of Vision, 22(3), 36. https://doi.org/10.1167/jov.22.3.36
Schmittwilken, L., & Maertens, M. (2022). Medium spatial frequencies mask edges most effectively. https://doi.org/10.13140/RG.2.2.11382.06726
Schmittwilken, L., Matic, M., Maertens, M., & Vincent, J. (2021, October). BRENCH: An open-source framework for b(r)enchmarking brightness models. Optica Fall Vision Meeting. https://doi.org/10.1167/jov.22.3.36
Best Paper Award (ECVP 2021)
For the public -
SCIoI at Girls’ Day/Mädchen-Zukunftstag: Meet a Vision Scientist – Einblicke in die Wahrnehmungsforschung (Lynn Schmittwilken)
For the public -
Lange Nacht der Wissenschaften 2021: The SCIoI Science Pub Quiz
