TY - GEN
T1 - Continuous-Time Intensity Estimation Using Event Cameras
AU - Scheerlinck, Cedric
AU - Barnes, Nick
AU - Mahony, Robert
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods. Code, Datasets and Video: https://cedric-scheerlinck.github.io/continuous-time-intensity-estimation.
AB - Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods. Code, Datasets and Video: https://cedric-scheerlinck.github.io/continuous-time-intensity-estimation.
UR - http://www.scopus.com/inward/record.url?scp=85066800372&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20873-8_20
DO - 10.1007/978-3-030-20873-8_20
M3 - Conference contribution
SN - 9783030208721
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 308
EP - 324
BT - Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Li, Hongdong
A2 - Mori, Greg
A2 - Schindler, Konrad
A2 - Jawahar, C.V.
PB - Springer Verlag
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -