Probabilistic Automatic Red Eye Detection and Correction

Willamowski, Jutta

doi:10.1109/icpr.2006.944

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…it contains a red eye. In our experiments the method described in [2] having these properties was, but other approaches can be considered as well.…”

Section: The Adaptive System's Designmentioning

confidence: 98%

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Safe Red-Eye Correction Plug-in Using Adaptive Methods

Marchesotti

Bresssan

2007

14th International Conference of Image Analysis and Processing - Workshops (ICIAPW 2007)

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An important issue with red-eye correction is that it might result in image degradation. This can be due to the detection of false positives or, even in the case of correct detection, to an inadequate correction technique. Three correction methods are proposed and compared according to their image degradation risk and their expected perceptual quality improvement. Based on those analyses an adaptive system is designed which selects the correction strategy dependent on those measures and the detection confidence. Finally, both qualitative (visual preferences) and quantitative (pixel counts on manual segmented images) evaluation results are shown.

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“…it contains a red eye. In our experiments the method described in [2] having these properties was, but other approaches can be considered as well.…”

Section: The Adaptive System's Designmentioning

confidence: 98%

“…in [2] or [3]) consists in combining the color channels (e.g. RGB) of the original image with the grayscale version g(x,y) of the image using the probability map P(…”

Section: Soft Pupil De-saturation (Spd)mentioning

confidence: 99%

Safe Red-Eye Correction Plug-in Using Adaptive Methods

Marchesotti

Bresssan

2007

14th International Conference of Image Analysis and Processing - Workshops (ICIAPW 2007)

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“…Usually the developers characterized own solutions via error rate, number of FP and FN [16]. Recently some papers discuss the various kinds of detection errors and their weights [15]. The editing or retouching quality was not discussed.…”

Section: Related Workmentioning

confidence: 99%

Automatic red eye correction and its quality metric

et al. 2008

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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 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.

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“…These methods include the eye orientation base on face detection [3]; eye orientation in complex background [4]; face detection base on complexion [5], etc. But they are awfully complex, and need long time to process.…”

Section: Introductionmentioning

confidence: 99%

A Novel Red-Eye Correction Method Based on AdaBoost Algorithm for Mobile Phone and Digital Camera

Feng

Lei

2009

2009 WRI International Conference on Communications and Mobile Computing

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Caused by light reflected off the subject's retina, red-eye is a troublesome problem in consumer photography. Correction of red eyes without any human intervention is an important task. There are some algorithms existing for red-eye detection, but almost all of them have less accuracy, in addition, they cannot support both high pixel and single red-eye. In this paper, a novel approach is proposed to eliminate red-eyes in the digital images automatically with a satisfactory result. This method gets the face region first by AdaBoost algorithm and then detects the red-eye on the top part of the face region, corrects the red-eye in the eye region for recovering the image's original color at last. Experiments in the platform of mobile phone and digital camera show that this method can eliminate the red-eye with high accuracy of 87%, which is higher than the best known technology of face detection base on complexion by 7%, and it can also support 8 million pixels image, moreover, the method has advantages of robustness and real-time computability.

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Probabilistic Automatic Red Eye Detection and Correction

Cited by 14 publications

References 4 publications

Safe Red-Eye Correction Plug-in Using Adaptive Methods

Safe Red-Eye Correction Plug-in Using Adaptive Methods

Automatic red eye correction and its quality metric

A Novel Red-Eye Correction Method Based on AdaBoost Algorithm for Mobile Phone and Digital Camera

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