Name
Abstract | ||
|---|---|---|
Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the red-eyes 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 eyes 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 non-eyes patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. The proposed method has been tested on large dataset of images achieving effective results in terms of hit rates maximization, false positives reduction and quality measure. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.1024 | Pattern Recognition |
Keywords | DocType | ISSN |
Gray codes,eye,feature extraction,filtering theory,image classification,image colour analysis,object detection,Gray code feature extraction,brightness reduction,desaturation,digital camera,false positive reduction,flashgun,hit rate maximization,image classification,image filtering pipeline,mobile devices,patch space clustering,quality measure,red eye detection,red eye removal,red-eye artifacts | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4244-7542-1 | 1 | 0.35 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Battiato, S. | 1 | 1 | 0.35 |
| Giovanni Maria Farinella | 2 | 41 | 2.49 |
| Guarnera, M. | 3 | 3 | 0.74 |
| Messina, G. | 4 | 1 | 0.35 |