Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
| Autor: | Thomas Pock, Gottfried Munda, Christian Reinbacher |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Machine vision Computer science Noise reduction Computer Vision and Pattern Recognition (cs.CV) Optical flow ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Iterative reconstruction law.invention 020901 industrial engineering & automation Artificial Intelligence law 0202 electrical engineering electronic engineering information engineering Computer vision Event (computing) business.industry 020206 networking & telecommunications Frame rate Neuromorphic engineering Asynchronous communication Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Timestamp Artificial intelligence business Manifold (fluid mechanics) Software |
| Zdroj: | BMVC |
| Popis: | Event cameras or neuromorphic cameras mimic the human perception system as they measure the per-pixel intensity change rather than the actual intensity level. In contrast to traditional cameras, such cameras capture new information about the scene at MHz frequency in the form of sparse events. The high temporal resolution comes at the cost of losing the familiar per-pixel intensity information. In this work we propose a variational model that accurately models the behaviour of event cameras, enabling reconstruction of intensity images with arbitrary frame rate in real-time. Our method is formulated on a per-event-basis, where we explicitly incorporate information about the asynchronous nature of events via an event manifold induced by the relative timestamps of events. In our experiments we verify that solving the variational model on the manifold produces high-quality images without explicitly estimating optical flow. Accepted to BMVC 2016 as oral presentation, 12 pages |
| Databáze: | OpenAIRE |
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