A Branch and Bound Approach to Correspondence and Grouping Problems
Identifieur interne : 000423 ( France/Analysis ); précédent : 000422; suivant : 000424A Branch and Bound Approach to Correspondence and Grouping Problems
Auteurs : Jean-Charles Bazin [Japon] ; Hongdong Li [Australie] ; Inso Kweon [Corée du Sud] ; Cédric Demonceaux [France] ; Pascal Vasseur [France] ; Katsushi Ikeuchi [Japon]Source :
- IEEE Transactions on Pattern Analysis and Machine Intelligence [ 0162-8828 ] ; 2013.
Descripteurs français
- Wicri :
- topic : Courrier électronique.
English descriptors
- mix :
Abstract
Data correspondence/grouping is a fundamental topic in computer vision. Finding feature correspondences is probably the most popular application of this topic and constitutes our main motivation. It is a key ingredient for various tasks including 3D reconstruction and object recognition. Existing feature correspondence methods are based on either local appearance similarity, or global geometric consistency, or a combination of both in some heuristic manner. None of these methods is fully satisfactory, especially with repetitive image textures or mis-matches. In this paper, we present a new algorithm that combines the benefits of both appearance-based and geometry-based methods, and mathematically guarantees a global optimization. Our algorithm accepts the two sets of features as input, and outputs the largest set of feature correspondences verifying both the appearance and geometric constraints. We formulate the problem as a mixed integer program, and solve it efficiently by a series of linear programs via branch-and-bound. We subsequently generalize our framework in the context of data correspondence/grouping under an unknown parametric model and show it can be applied to certain classes of computer vision problems. Our algorithm has been validated successfully on synthesized data and challenging real images.
Url:
DOI: 10.1109/TPAMI.2012.264
Affiliations:
- Australie, Corée du Sud, France, Japon
- 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-00829611Le document en format XML
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Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName></affiliation></author><author><name sortKey="Ikeuchi, Katsushi" sort="Ikeuchi, Katsushi" uniqKey="Ikeuchi K" first="Katsushi" last="Ikeuchi">Katsushi Ikeuchi</name><affiliation wicri:level="1"><hal:affiliation type="laboratory" xml:id="struct-50647" status="VALID"><orgName>Institute of Industrial Science</orgName><desc><address><addrLine>4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</addrLine><country key="JP"></country></address><ref type="url">http://dfs.iis.u-tokyo.ac.jp/</ref></desc><listRelation><relation active="#struct-254193" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-254193" type="direct"><org type="institution" xml:id="struct-254193" status="VALID"><orgName>University of Tokyo [Tokyo]</orgName><desc><address><addrLine>7-3-1 Hongo, Bunkyo, Tokyo 113-8654</addrLine><country 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électronique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Data correspondence/grouping is a fundamental topic in computer vision. Finding feature correspondences is probably the most popular application of this topic and constitutes our main motivation. It is a key ingredient for various tasks including 3D reconstruction and object recognition. Existing feature correspondence methods are based on either local appearance similarity, or global geometric consistency, or a combination of both in some heuristic manner. None of these methods is fully satisfactory, especially with repetitive image textures or mis-matches. In this paper, we present a new algorithm that combines the benefits of both appearance-based and geometry-based methods, and mathematically guarantees a global optimization. Our algorithm accepts the two sets of features as input, and outputs the largest set of feature correspondences verifying both the appearance and geometric constraints. We formulate the problem as a mixed integer program, and solve it efficiently by a series of linear programs via branch-and-bound. We subsequently generalize our framework in the context of data correspondence/grouping under an unknown parametric model and show it can be applied to certain classes of computer vision problems. Our algorithm has been validated successfully on synthesized data and challenging real images.</div></front></TEI><affiliations><list><country><li>Australie</li><li>Corée du Sud</li><li>France</li><li>Japon</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="Japon"><noRegion><name sortKey="Bazin, Jean Charles" sort="Bazin, Jean Charles" uniqKey="Bazin J" first="Jean-Charles" last="Bazin">Jean-Charles Bazin</name></noRegion><name sortKey="Ikeuchi, Katsushi" sort="Ikeuchi, Katsushi" uniqKey="Ikeuchi K" first="Katsushi" last="Ikeuchi">Katsushi Ikeuchi</name></country><country name="Australie"><noRegion><name sortKey="Li, Hongdong" sort="Li, Hongdong" uniqKey="Li H" first="Hongdong" last="Li">Hongdong Li</name></noRegion></country><country name="Corée du Sud"><noRegion><name sortKey="Kweon, Inso" sort="Kweon, Inso" uniqKey="Kweon I" first="Inso" last="Kweon">Inso Kweon</name></noRegion></country><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="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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