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- W2000589728 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." @default.
- W2000589728 created "2016-06-24" @default.
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- W2000589728 date "2010-09-01" @default.
- W2000589728 modified "2023-09-25" @default.
- W2000589728 title "Red-eyes removal through cluster based Linear Discriminant Analysis" @default.
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- W2000589728 doi "https://doi.org/10.1109/icip.2010.5649987" @default.
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