像素
事件(粒子物理)
计算机视觉
计算机科学
人工智能
对比度(视觉)
异步通信
校准
图像分辨率
强度(物理)
数学
光学
统计
物理
计算机网络
量子力学
作者
Ziwei Wang,Yonhon Ng,Pieter van Goor,Robert Mahony
出处
期刊:arXiv: Computer Vision and Pattern Recognition
日期:2020-12-17
被引量:2
摘要
Event cameras output asynchronous events to represent intensity changes with a high temporal resolution, even under extreme lighting conditions. Currently, most of the existing works use a single contrast threshold to estimate the intensity change of all pixels. However, complex circuit bias and manufacturing imperfections cause biased pixels and mismatch contrast threshold among pixels, which may lead to undesirable outputs. In this paper, we propose a new event camera model and two calibration approaches which cover event-only cameras and hybrid image-event cameras. When intensity images are simultaneously provided along with events, we also propose an efficient online method to calibrate event cameras that adapts to time-varying event rates. We demonstrate the advantages of our proposed methods compared to the state-of-the-art on several different event camera datasets.
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