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...

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Main Authors: Guarnera Mirko, Messina Giuseppe, Battiato Sebastiano, Farinella GiovanniMaria, Rav&#236; Daniele
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Image and Video Processing
Online Access:http://jivp.eurasipjournals.com/content/2010/909043
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spelling doaj-823f283d9c39464eb1ef59bf589a2c842020-11-24T22:10:05ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-51761687-52812010-01-0120101909043Red-Eyes Removal through Cluster-Based Boosting on Gray CodesGuarnera MirkoMessina GiuseppeBattiato SebastianoFarinella GiovanniMariaRav&#236; 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
collection DOAJ
language English
format Article
sources DOAJ
author Guarnera Mirko
Messina Giuseppe
Battiato Sebastiano
Farinella GiovanniMaria
Rav&#236; Daniele
spellingShingle Guarnera Mirko
Messina Giuseppe
Battiato Sebastiano
Farinella GiovanniMaria
Rav&#236; Daniele
Red-Eyes Removal through Cluster-Based Boosting on Gray Codes
EURASIP Journal on Image and Video Processing
author_facet Guarnera Mirko
Messina Giuseppe
Battiato Sebastiano
Farinella GiovanniMaria
Rav&#236; Daniele
author_sort Guarnera Mirko
title Red-Eyes Removal through Cluster-Based Boosting on Gray Codes
title_short 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_sort red-eyes removal through cluster-based boosting on gray codes
publisher SpringerOpen
series EURASIP Journal on Image and Video Processing
issn 1687-5176
1687-5281
publishDate 2010-01-01
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>
url http://jivp.eurasipjournals.com/content/2010/909043
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AT messinagiuseppe redeyesremovalthroughclusterbasedboostingongraycodes
AT battiatosebastiano redeyesremovalthroughclusterbasedboostingongraycodes
AT farinellagiovannimaria redeyesremovalthroughclusterbasedboostingongraycodes
AT rav236daniele redeyesremovalthroughclusterbasedboostingongraycodes
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