IMAGE ENHANCEMENT BY REGION DETECTION ON CFA DATA IMAGES

S. Battiato, S. Cariolo, G. Gallo, G. Di Blasi

2007

Abstract

The paper proposes a new method devoted to identify specific semantic regions on CFA (Color Filtering Array) data images representing natural scenes. Making use of collected statistics over a large dataset of high quality natural images, the method uses spatial features and the Principal Component Analysis (PCA) in the HSL and normalized-RG color spaces. The classes considered, taking into account “visual significance”, are skin, vegetation, blue sky and sea. Semantic information are obtained on pixel basis leading to meaningful regions although not spatially coherent. Such information is used for automatic color rendition of natural digital images based on adaptive color correction. The overall method outperforms previous results providing reliable information validated by measured and subjective experiments.

References

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Paper Citation


in Harvard Style

Battiato S., Cariolo S., Gallo G. and Di Blasi G. (2007). IMAGE ENHANCEMENT BY REGION DETECTION ON CFA DATA IMAGES . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 200-207. DOI: 10.5220/0002067402000207


in Bibtex Style

@conference{visapp07,
author={S. Battiato and S. Cariolo and G. Gallo and G. Di Blasi},
title={IMAGE ENHANCEMENT BY REGION DETECTION ON CFA DATA IMAGES},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={200-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002067402000207},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - IMAGE ENHANCEMENT BY REGION DETECTION ON CFA DATA IMAGES
SN - 978-972-8865-74-0
AU - Battiato S.
AU - Cariolo S.
AU - Gallo G.
AU - Di Blasi G.
PY - 2007
SP - 200
EP - 207
DO - 10.5220/0002067402000207