scholarly journals High Speed and High Dynamic Range Video with an Event Camera

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Rebecq ◽  
Rene Ranftl ◽  
Vladlen Koltun ◽  
Davide Scaramuzza
Keyword(s):  
High Speed ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Event Camera ◽  
High Dynamic ◽  
High Dynamic Range Video

High Speed High Dynamic Range Video

2017 ◽  
Vol 17 (8) ◽  
pp. 2472-2480 ◽  
Author(s):  
David J. Griffiths ◽  
Alfred Wicks
Keyword(s):  
High Speed ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic ◽  
High Dynamic Range Video

scholarly journals The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM

2017 ◽  
Vol 36 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Elias Mueggler ◽  
Henri Rebecq ◽  
Guillermo Gallego ◽  
Tobi Delbruck ◽  
Davide Scaramuzza
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Dynamic Range ◽  
Ground Truth ◽  
High Dynamic Range ◽  
High Temporal Resolution ◽  
Event Camera ◽  
High Dynamic ◽  
Event Based ◽  
New Algorithms

New vision sensors, such as the dynamic and active-pixel vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e. rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.

Appearance reproduction of high-dynamic-range video based on human visual models

2007 ◽  
Vol 90 (11) ◽  
pp. 55-64
Author(s):  
Keita Hirai ◽  
Toshiya Nakaguchi ◽  
Norimichi Tsumura ◽  
Yoichi Miyake
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Visual Models ◽  
High Dynamic ◽  
High Dynamic Range Video

High dynamic range video

2003 ◽  
Vol 22 (3) ◽  
pp. 319-325 ◽  
Author(s):  
Sing Bing Kang ◽  
Matthew Uyttendaele ◽  
Simon Winder ◽  
Richard Szeliski
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic ◽  
High Dynamic Range Video

High-Speed, High Dynamic-Range Optical Sensor Arrays

2009 ◽  
Vol 56 (3) ◽  
pp. 1069-1075 ◽  
Author(s):  
Stuart Kleinfelder ◽  
Shiuh-Hua Wood Chiang ◽  
Wei Huang ◽  
Ashish Shah ◽  
Kris Kwiatkowski
Keyword(s):  
Optical Sensor ◽  
High Speed ◽  
Dynamic Range ◽  
Sensor Arrays ◽  
High Dynamic Range ◽  
High Dynamic
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