28 January 2008 Automatic red eye correction and its quality metric
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The red eye artifacts are troublesome defect of amateur photos. Correction of red eyes during printing without user intervention and making photos more pleasant for an observer are important tasks. The novel efficient technique of automatic correction of red eyes aimed for photo printers is proposed. This algorithm is independent from face orientation and capable to detect paired red eyes as well as single red eyes. The approach is based on application of 3D tables with typicalness levels for red eyes and human skin tones and directional edge detection filters for processing of redness image. Machine learning is applied for feature selection. For classification of red eye regions a cascade of classifiers including Gentle AdaBoost committee from Classification and Regression Trees (CART) is applied. Retouching stage includes desaturation, darkening and blending with initial image. Several versions of approach implementation using trade-off between detection and correction quality, processing time, memory volume are possible. The numeric quality criterion of automatic red eye correction is proposed. This quality metric is constructed by applying Analytic Hierarchy Process (AHP) for consumer opinions about correction outcomes. Proposed numeric metric helped to choose algorithm parameters via optimization procedure. Experimental results demonstrate high accuracy and efficiency of the proposed algorithm in comparison with existing solutions.
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Ilia V. Safonov, Ilia V. Safonov, Michael N. Rychagov, Michael N. Rychagov, KiMin Kang, KiMin Kang, Sang Ho Kim, Sang Ho Kim, "Automatic red eye correction and its quality metric", Proc. SPIE 6807, Color Imaging XIII: Processing, Hardcopy, and Applications, 68070W (28 January 2008); doi: 10.1117/12.758603; https://doi.org/10.1117/12.758603

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