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Event-based Deep Learning
The goal of this project is to explore new algorithms for processing events within a deep-learning context.
Keywords: Deep Learning, DVS, DAVIS, Learning, Asynchronous, low-latency, high-dynamic-range, computer vision, event camera
Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high-dynamic-range robotic applications. The goal of this project is to explore new algorithms for processing events within a deep-learning context. The goal of the project should be implementing and comparing different frameworks for processing events, and applying them to challenging tasks, such as optical flow prediction. This is a project with considerable room for creativity. Experience in coding image processing algorithms in C++ and experience with learning frameworks in python is required.
Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high-dynamic-range robotic applications. The goal of this project is to explore new algorithms for processing events within a deep-learning context. The goal of the project should be implementing and comparing different frameworks for processing events, and applying them to challenging tasks, such as optical flow prediction. This is a project with considerable room for creativity. Experience in coding image processing algorithms in C++ and experience with learning frameworks in python is required.
The goal of this project is to explore new algorithms for processing events within a deep-learning context.
The goal of this project is to explore new algorithms for processing events within a deep-learning context.
Daniel Gehrig (dgehrig at ifi.uzh.ch), Antonio Loquercio (antonilo (at) ifi.uzh.ch)
Daniel Gehrig (dgehrig at ifi.uzh.ch), Antonio Loquercio (antonilo (at) ifi.uzh.ch)