Central catadioptric image processing with geodesic metric
Identifieur interne : 000666 ( France/Analysis ); précédent : 000665; suivant : 000667Central catadioptric image processing with geodesic metric
Auteurs : Cédric Demonceaux [France] ; Pascal Vasseur [France] ; Yohan Fougerolle [France]Source :
- Image and Vision Computing [ 0262-8856 ] ; 2011.
English descriptors
Abstract
Because of the distortions produced by the insertion of a mirror, catadioptric images cannot be processed similarly to classical perspective images. Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations.
Url:
DOI: 10.1016/j.imavis.2011.09.007
Affiliations:
- France
- Haute-Normandie, Région Bourgogne, Région Normandie
- Dijon, Le Havre, Rouen
- Université de Bourgogne, Université de Bourgogne Franche-Comté, Université de Rouen, Université du Havre
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Hal:hal-00647150Le document en format XML
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Now, although the equivalence between such images and spherical images is well known, the use of spherical harmonic analysis often leads to image processing methods which are more difficult to implement. In this paper, we propose to define catadioptric image processing from the geodesic metric on the unitary sphere. We show that this definition allows to adapt very simply classical image processing methods. We focus more particularly on image gradient estimation, interest point detection, and matching. More generally, the proposed approach extends traditional image processing techniques based on Euclidean metric to central catadioptric images. We show in this paper the efficiency of the approach through different experimental results and quantitative evaluations.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Bourgogne</li><li>Région Normandie</li></region><settlement><li>Dijon</li><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Bourgogne</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Bourgogne"><name sortKey="Demonceaux, Cedric" sort="Demonceaux, Cedric" uniqKey="Demonceaux C" first="Cédric" last="Demonceaux">Cédric Demonceaux</name></region><name sortKey="Fougerolle, Yohan" sort="Fougerolle, Yohan" uniqKey="Fougerolle Y" first="Yohan" last="Fougerolle">Yohan Fougerolle</name><name sortKey="Vasseur, Pascal" sort="Vasseur, Pascal" uniqKey="Vasseur P" first="Pascal" last="Vasseur">Pascal Vasseur</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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