Proceedings ArticleDOI
Automatic red eye correction and its quality metric
Ilia V. Safonov,Michael N. Rychagov,Ki-min Kang,Sang Ho Kim +3 more
- Vol. 6807
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TLDR
The novel efficient technique of automatic correction of red eyes aimed for photo printers is proposed, independent from face orientation and capable to detect paired red eyes as well as single red eyes.Abstract:
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 features 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 requirements are possible. The numeric quality criterion of automatic red eye correction is proposed. This quality metric is constructed by applying Analytic Hierarchy Process (AHP) to consumer opinions about correction outcomes. Proposed numeric metric helps to choose algorithm parameters via optimization procedure. Experimental results demonstrate high accuracy and efficiency of the proposed algorithm in comparison with existing solutions.read more
Citations
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Journal ArticleDOI
A Review of Redeye Detection and Removal in Digital Images Through Patents
TL;DR: This paper summarizes the history and the state of the art of redeye detection and correction in digital photography, starting from the analysis of patents, and describes the main approaches with the help of flowcharts and figures.
Proceedings ArticleDOI
Red-eyes removal through cluster based Linear Discriminant Analysis
TL;DR: 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.
Journal ArticleDOI
Red-eyes removal through cluster-based boosting on gray codes
TL;DR: Experimental results on a large dataset of images demonstrate the effectiveness of the proposed pipeline that outperforms other existing solutions in terms of hit rates maximization, false positives reduction, and quality measure.
Proceedings ArticleDOI
Boosting Gray Codes for Red Eyes Removal
TL;DR: The proposed method makes use of three main steps to identify and remove red-eyes: red eyes candidates are extracted from the input image by using an image filtering pipeline, a set of classifiers is learned on gray code features extracted in the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches.
Patent
Method and apparatus for filtering red and/or golden eye artifacts
TL;DR: In this article, the authors proposed a method to filter red and/or golden eye artifacts by converting the digital values of a pixel into a Gray code representation, overall obtaining a plurality of bit maps from said pixel, each bit map being associated with a respective bit of said Gray code.
References
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Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
Journal ArticleDOI
Seeded region growing
Richard Adams,Leanne Bischof +1 more
TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.
Book
Handbook of Image Quality: Characterization and Prediction
TL;DR: Characterization and quality: can image quality be usefully quantified, and the probablistic nature of perception just noticable differences.
Proceedings ArticleDOI
Automatic red-eye detection and correction
M. Gaubatz,R. Ulichney +1 more
TL;DR: A method is presented to automatically detect and correct redeye in digital images, where faces are detected with a cascade of multi-scale classifiers, and red-eye pixels are located with several refining masks computed over the facial region.