NDLI: Red-eyes removal through cluster based Linear Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Battiato, S. Farinella, G.M. Guarnera, M. Messina, G. Ravì, D.
Copyright Year	2010
Description	Author affiliation: Università degli Studi di Catania, Dipartimento di Matematica e Informatica, Viale A. Doria 6, 95125, Italy (Battiato, S.; Farinella, G.M.) \|\| STMicroelectronics, Advanced System Technology, Stradale Primosole 50, 95121 - Catania, Italy (Guarnera, M.; Messina, G.; Ravì, D.)
Abstract	Red-eye artifact is a well-known problem in digital photography. Since the large diffusion of mobile devices with embedded camera and flashgun, automatic detection and correction of red-eyes have become an important task. In this paper we describe a technique that makes use of three steps to identify and correct red-eyes. First, red-eye candidates are extracted from the input image by using simple color segmentation coupled with geometrical constraints. A set of linear discriminant classifiers is then learned on the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches. The proposed cluster-based Linear Discriminant Analysis is used to deal with the multi-modally nature of the input space. The third step of the pipeline is devoted to artifacts correction through de-saturation and brightness reduction. Experimental results on a large dataset of images demonstrate the effectiveness of the pro- posed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction and ad-hoc quality measure.
Starting Page	2185
Ending Page	2188
File Size	158338
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5649987
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Pipelines Pixel Ash Shape Brightness Linear discriminant analysis Multi-modally Distributed Classes Red-eyes Detection Red-eyes Removal Linear Discriminant Analysis
Content Type	Text
Resource Type	Article

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