Computational imaging on the electric grid

Mark Sheinin; Yoav Y. Schechner; Kiriakos N. Kutulakos

doi:https://doi.org/10.1109/CVPR.2017.254

Computational imaging on the electric grid

Mark Sheinin, Yoav Y. Schechner, Kiriakos N. Kutulakos

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

Original language	English
Title of host publication	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Pages	2363-2372
Number of pages	10
ISBN (Electronic)	9781538604571
DOIs	https://doi.org/10.1109/CVPR.2017.254
State	Published - 6 Nov 2017
Event	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017

Publication series

Name	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Volume	2017-January

Conference

Conference	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Country/Territory	United States
City	Honolulu
Period	21/07/17 → 26/07/17

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

https://doi.org/10.1109/CVPR.2017.254

Cite this

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title = "Computational imaging on the electric grid",

abstract = "Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.",

author = "Mark Sheinin and Schechner, {Yoav Y.} and Kutulakos, {Kiriakos N.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

year = "2017",

month = nov,

day = "6",

doi = "https://doi.org/10.1109/CVPR.2017.254",

language = "الإنجليزيّة",

series = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",

pages = "2363--2372",

booktitle = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",

}

Sheinin, M, Schechner, YY & Kutulakos, KN 2017, Computational imaging on the electric grid. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 2363-2372, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, United States, 21/07/17. https://doi.org/10.1109/CVPR.2017.254

Computational imaging on the electric grid. / Sheinin, Mark; Schechner, Yoav Y.; Kutulakos, Kiriakos N.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017. p. 2363-2372 (Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Computational imaging on the electric grid

AU - Sheinin, Mark

AU - Schechner, Yoav Y.

AU - Kutulakos, Kiriakos N.

PY - 2017/11/6

Y1 - 2017/11/6

N2 - Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

AB - Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.

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DO - https://doi.org/10.1109/CVPR.2017.254

M3 - منشور من مؤتمر

T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

SP - 2363

EP - 2372

BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Y2 - 21 July 2017 through 26 July 2017

ER -

Computational imaging on the electric grid

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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