Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)

Exposure Evaluation Method Based on Histogram Statistics

Authors
Wen Chen, Xinglong Li
Corresponding Author
Wen Chen
Available Online April 2017.
DOI
10.2991/eame-17.2017.68How to use a DOI?
Keywords
exposure evaluation;image histogram; third moment; middle gray
Abstract

Aiming at the inadequate or excessive exposure problem existed in the imaging system in a scene where there is no specific background or foreground, this paper presents a histogram-based exposure evaluation method. With analysis of statistics features of histograms in different exposure type images, the third moment about middle gray is used to evaluate the exposure effect. For color images, the histogram is calculated only in the component which holds the largest mean brightness considering of efficiency. Experimental result shows that the proposed method can evaluate the exposure effect without the support of database, and it has low complexity and is easy to implement.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-332-6
ISSN
2352-5401
DOI
10.2991/eame-17.2017.68How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wen Chen
AU  - Xinglong Li
PY  - 2017/04
DA  - 2017/04
TI  - Exposure Evaluation Method Based on Histogram Statistics
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 290
EP  - 293
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.68
DO  - 10.2991/eame-17.2017.68
ID  - Chen2017/04
ER  -