Red-Eyes Removal through Cluster-Based Boosting on Gray Codes
<p/> <p>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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Online Access: | http://jivp.eurasipjournals.com/content/2010/909043 |
| _version_ | 1818012697107628032 |
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| author | Guarnera Mirko Messina Giuseppe Battiato Sebastiano Farinella GiovanniMaria Ravì Daniele |
| author_facet | Guarnera Mirko Messina Giuseppe Battiato Sebastiano Farinella GiovanniMaria Ravì Daniele |
| author_sort | Guarnera Mirko |
| collection | DOAJ |
| description | <p/> <p>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 non-eyes 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 <it>eye</it> versus <it>non-eye</it> 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.</p> |
| first_indexed | 2024-04-14T06:23:36Z |
| format | Article |
| id | doaj.art-823f283d9c39464eb1ef59bf589a2c84 |
| institution | Directory Open Access Journal |
| issn | 1687-5176 1687-5281 |
| language | English |
| last_indexed | 2024-04-14T06:23:36Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-823f283d9c39464eb1ef59bf589a2c842022-12-22T02:07:56ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812010-01-0120101909043Red-Eyes Removal through Cluster-Based Boosting on Gray CodesGuarnera MirkoMessina GiuseppeBattiato SebastianoFarinella GiovanniMariaRavì Daniele<p/> <p>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 non-eyes 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 <it>eye</it> versus <it>non-eye</it> 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.</p>http://jivp.eurasipjournals.com/content/2010/909043 |
| spellingShingle | Guarnera Mirko Messina Giuseppe Battiato Sebastiano Farinella GiovanniMaria Ravì Daniele Red-Eyes Removal through Cluster-Based Boosting on Gray Codes EURASIP Journal on Image and Video Processing |
| title | Red-Eyes Removal through Cluster-Based Boosting on Gray Codes |
| title_full | Red-Eyes Removal through Cluster-Based Boosting on Gray Codes |
| title_fullStr | Red-Eyes Removal through Cluster-Based Boosting on Gray Codes |
| title_full_unstemmed | Red-Eyes Removal through Cluster-Based Boosting on Gray Codes |
| title_short | Red-Eyes Removal through Cluster-Based Boosting on Gray Codes |
| title_sort | red eyes removal through cluster based boosting on gray codes |
| url | http://jivp.eurasipjournals.com/content/2010/909043 |
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