Guillermo Gallego

TU Berlin, Computer Vision

Guillermo Gallego is Professor of Robotic Interactive Perception at Technische Universität Berlin and Einstein Center Digital Future (ECDF). For SCIoI, he works on computer vision and robotics. He focuses on robot perception and on optimization methods for interdisciplinary imaging and control problems. Inspired by the human visual system, he works toward improving the perception systems of artificial agents, endowing them with intelligence to transform raw sensor data into knowledge, to provide autonomy in changing environments.

At SCIoI, Guillermo works on Project 36.


SCIoI Publications:

Zhang, Z., Yezzi, A., & Gallego, G. (2022). Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Shiba, S., Aoki, Y., & Gallego, G. (2022). Secrets of Event-Based Optical Flow. European Conference on Computer Vision (ECCV), 628–645.
Shiba, S., Aoki, Y., & Gallego, G. (2022). A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework. Advanced Intelligent Systems, 11.
Shiba, S., Aoki, Y., & Gallego, G. (2022). Fast Event-based Optical Flow Estimation by Triplet Matching. Signal Processing Letters, 29, 2712–2716.
Shiba, S., Hamann, F., Aoki, Y., & Gallego, G. (2023). Event-based Background-Oriented Schlieren. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–16.
Hamann, F., Ghosh, S., Martínez, I. J., Hart, T., Kacelnik, A., & Gallego, G. (2023). Low-power, Continuous Remote Behavioral Localization with Event Cameras. arXiv.
Hamann, F., & Gallego, G. (2022). Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras. International Conference on Pattern Recognition (ICPR), Workshop on Visual observation and analysis of Vertebrate And Insect Behavior.
Gu, C., Learned-Miller, E., Gallego, G., Sheldon, D., & Bideau, P. (2021). The Spatio-Temporal Poisson Point process: A simple Model for the Alignment of Event Camera Data. International Conference on Computer Vision (ICCV), 13495–13504.
Ghosh, S., & Gallego, G. (2022). Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion. ECCVW Ego4D 2022.