Abstract
Red eye artifacts are a well-known problem in digital photography. Small compact devices and point-and-click usage, typical of non-professional photography, greatly increase the likelihood for red eyes to appear in acquired images. Automatic detection of red eyes is a very challenging task, due to the variability of the phenomenon and the general difficulty in reliably discerning the shape of eyes.
This paper presents a method for discriminating between red eyes and other objects in a set of red eye candidates. The proposed method performs feature-based image analysis and classification just considering the bag-of-keypoints paradigm. Experiments involving different keypoint detectors/descriptors are performed. Achieved results are presented, as well as directions for future work.
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Battiato, S., Farinella, G.M., Guarnera, M., Messina, G., Ravì, D.: Red-eyes detection through cluster based linear discriminant analysis (to appear)
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Battiato, S., Guarnera, M., Meccio, T., Messina, G. (2009). Red Eye Detection through Bag-of-Keypoints Classification. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_57
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DOI: https://doi.org/10.1007/978-3-642-04146-4_57
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