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Data-driven Event Generation from Images
In this project, the student applies concepts from current advances in image generation to create artificial events from standard frames. Multiple state-of-the-art deep learning methods will be explored in the scope of this project.
Keywords: Computer Vision, Event Cameras, Deep Learning
Event cameras represent a significant advancement in imaging technology, capturing scenes based on changes in light intensity rather than at fixed intervals. This project aims to address the challenge of limited event-based datasets by generating synthetic events from traditional frame-based data. By employing data-driven deep learning techniques, we plan to create high-fidelity artificial events that closely mimic real-world occurrences, reducing the gap between simulated and actual event data.
Event cameras represent a significant advancement in imaging technology, capturing scenes based on changes in light intensity rather than at fixed intervals. This project aims to address the challenge of limited event-based datasets by generating synthetic events from traditional frame-based data. By employing data-driven deep learning techniques, we plan to create high-fidelity artificial events that closely mimic real-world occurrences, reducing the gap between simulated and actual event data.
In this project, the student applies current state-of-the-art deep learning models for image generation to create artificial events from standard frames. In the scope of the project, the student will obtain a deep understanding of event cameras to generate realistic events. Since multiple state-of-the-art deep learning methods will be explored, a good background in deep learning is required. If you are interested, we are happy to provide more details.
In this project, the student applies current state-of-the-art deep learning models for image generation to create artificial events from standard frames. In the scope of the project, the student will obtain a deep understanding of event cameras to generate realistic events. Since multiple state-of-the-art deep learning methods will be explored, a good background in deep learning is required. If you are interested, we are happy to provide more details.