Revisiting gray pixel for statistical illumination estimation

Yanlin Qian; Said Pertuz; Jarno Nikkanen; Joni-Kristian Kämäräinen; Jiri Matas

doi:10.5220/0007406900360046

Revisiting gray pixel for statistical illumination estimation

Yanlin Qian, Said Pertuz, Jarno Nikkanen, Joni-Kristian Kämäräinen, Jiri Matas

Computing Sciences

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

17 Citations (Scopus)

22 Downloads (Pure)

Abstract

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel – MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

Original language	English
Title of host publication	VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Editors	Andreas Kerren, Christophe Hurter, Jose Braz
Publisher	SCITEPRESS
Pages	36-46
Number of pages	11
ISBN (Electronic)	9789897583544
DOIs	https://doi.org/10.5220/0007406900360046
Publication status	Published - 2019
Publication type	A4 Article in conference proceedings
Event	International Conference on Computer Vision Theory and Applications - Prague, Czech Republic Duration: 25 Feb 2019 → 27 Feb 2019

Conference

Conference	International Conference on Computer Vision Theory and Applications
Country/Territory	Czech Republic
City	Prague
Period	25/02/19 → 27/02/19

Keywords

Color Constancy
Gray Pixel
Illumination Estimation

Publication forum classification

Publication forum level 0

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

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10.5220/0007406900360046Licence: CC BY-NC-ND

VISAPP_2019_201
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 license. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
Final published version, 3.98 MBLicence: CC BY-NC-ND

http://urn.fi/URN:NBN:fi:tty-201908282042Licence: CC BY-NC-ND

Cite this

Qian, Y., Pertuz, S., Nikkanen, J., Kämäräinen, J.-K., & Matas, J. (2019). Revisiting gray pixel for statistical illumination estimation. In A. Kerren, C. Hurter, & J. Braz (Eds.), VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 36-46). SCITEPRESS. https://doi.org/10.5220/0007406900360046

@inproceedings{425048405bed4e2a9c0132ae0a79a752,

title = "Revisiting gray pixel for statistical illumination estimation",

abstract = "We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel – MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.",

keywords = "Color Constancy, Gray Pixel, Illumination Estimation",

author = "Yanlin Qian and Said Pertuz and Jarno Nikkanen and Joni-Kristian K{\"a}m{\"a}r{\"a}inen and Jiri Matas",

year = "2019",

doi = "10.5220/0007406900360046",

language = "English",

pages = "36--46",

editor = "Andreas Kerren and Christophe Hurter and Jose Braz",

booktitle = "VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",

publisher = "SCITEPRESS",

note = "International Conference on Computer Vision Theory and Applications ; Conference date: 25-02-2019 Through 27-02-2019",

}

Qian, Y, Pertuz, S, Nikkanen, J, Kämäräinen, J-K & Matas, J 2019, Revisiting gray pixel for statistical illumination estimation. in A Kerren, C Hurter & J Braz (eds), VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS, pp. 36-46, International Conference on Computer Vision Theory and Applications, Prague, Czech Republic, 25/02/19. https://doi.org/10.5220/0007406900360046

Revisiting gray pixel for statistical illumination estimation. / Qian, Yanlin; Pertuz, Said; Nikkanen, Jarno et al.
VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. ed. / Andreas Kerren; Christophe Hurter; Jose Braz. SCITEPRESS, 2019. p. 36-46.

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

TY - GEN

T1 - Revisiting gray pixel for statistical illumination estimation

AU - Qian, Yanlin

AU - Pertuz, Said

AU - Nikkanen, Jarno

AU - Kämäräinen, Joni-Kristian

AU - Matas, Jiri

PY - 2019

Y1 - 2019

N2 - We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel – MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

AB - We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel – MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.

KW - Color Constancy

KW - Gray Pixel

KW - Illumination Estimation

U2 - 10.5220/0007406900360046

DO - 10.5220/0007406900360046

M3 - Conference contribution

SP - 36

EP - 46

BT - VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

A2 - Kerren, Andreas

A2 - Hurter, Christophe

A2 - Braz, Jose

PB - SCITEPRESS

T2 - International Conference on Computer Vision Theory and Applications

Y2 - 25 February 2019 through 27 February 2019

ER -

Revisiting gray pixel for statistical illumination estimation

Abstract

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Keywords

Publication forum classification

ASJC Scopus subject areas

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