output
20152025
most citedEvent-based Vision: A Survey

2.3k citations

Showing 2020Show all

8 papers · 1 filter

cs.DC2020

Edge (of the Earth) Replication: Optimizing Content Delivery in Large LEO Satellite Communication Networks

Tobias Pfandzelter, David Bermbach

Large low earth orbit (LEO) satellite networks such as SpaceX's Starlink constellation promise to deliver low-latency, high-bandwidth Internet access with global coverage. As an al…

cs.CV2020★ 78 cited

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

Yi Zhou, Guillermo Gallego, Xiuyuan Lu +2

Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or ex…

cs.DC2020★ 37 cited

MockFog 2.0: Automated Execution of Fog Application Experiments in the Cloud

Jonathan Hasenburg, Martin Grambow, David Bermbach

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing and benchmarking f…

cs.DC2020★ 23 cited

From Zero to Fog: Efficient Engineering of Fog-Based Internet of Things Applications

Tobias Pfandzelter, Jonathan Hasenburg, David Bermbach

In IoT data processing, cloud computing alone does not suffice due to latency constraints, bandwidth limitations, and privacy concerns. By introducing intermediary nodes closer to…

cs.CV2020★ 246 cited

Event-based Stereo Visual Odometry

Yi Zhou, Guillermo Gallego, Shaojie Shen

Event-based cameras are bio-inspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution. T…

cs.CY2020★ 38 cited

SimRa: Using Crowdsourcing to Identify Near Miss Hotspots in Bicycle Traffic

Ahmet-Serdar Karakaya, Jonathan Hasenburg, David Bermbach

An increased modal share of bicycle traffic is a key mechanism to reduce emissions and solve traffic-related problems. However, a lack of (perceived) safety keeps people from using…