The Strength of Weak Learnability
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Digital photography with flash and no-flash image pairs
ACM SIGGRAPH 2004 Papers
An Efficient Automatic Redeye Detection and Correction Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Red Eye Detection through Bag-of-Keypoints Classification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the redeyes artifacts have de facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red eyes. First, red-eye candidates are extracted from the input image by using an image filtering pipeline. A set of classifiers is then learned on gray code features extracted in the clustered patches space and hence employed to distinguish between eyes and noneyes patches. Specifically, for each cluster the gray code of the red-eyes candidate is computed and some discriminative gray code bits are selected employing a boosting approach. The selected gray code bits are used during the classification to discriminate between eye versus non-eye patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. 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.