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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3A3L7LB
Repositorysid.inpe.br/sibgrapi/2011/07.10.22.27
Metadatasid.inpe.br/sibgrapi/2011/07.10.22.27.15
Sitesibgrapi.sid.inpe.br
Citation KeyBrahmachariSark:2011:ViClWi
Author1 Brahmachari, Aveek Shankar
2 Sarkar, Sudeep
Affiliation1 University of South Florida, Computer Science and Engineering
2 University of South Florida, Computer Science and Engineering
TitleView Clustering of Wide-Baseline N-Views for Photo Tourism
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Year2011
EditorLewiner, Thomas
Torres, Ricardo
Book TitleProceedings
DateAug. 28 - 31, 2011
Publisher CityLos Alamitos
PublisherIEEE Computer Society Conference Publishing Services
Conference LocationMaceió
KeywordsComputer Vision, Image Collection, Epipolar Geometry, Photo Organization.
AbstractThe problem of view clustering is concerned with finding connected sets of overlapping views in a collection of photographs. The view clusters can be used to organize a photo collection, traverse through a collection, or for 3D structure estimation. For large datasets, geometric matching of all image pairs via pose estimation to decide on content overlap is not viable. The problem becomes even more acute if the views in the collection are separated by wide baselines, i.e. we do not have a dense view sampling of the 3D scene that leads to increase in computational cost of epipolar geometry estimation and matching. We propose an efficient algorithm for clustering of such many weakly overlapping views, based on opportunistic use of epipolar geometry estimation for only a limited number of image pairs. We cast the problem of view clustering as finding a tree structure graph over the views, whose weighted links denote likelihood of view overlap. The optimization is done in an iterative fashion starting from an minimum spanning tree based on photometric distances between image pairs. At each iteration step, we rule out edges with low confidence of overlap between the respective views, based on epipolar geometry estimates. The minimum spanning tree is recomputed and the process is repeated until there is no further change in the link structure. We show results on the images in the 2010 Nokia Grand Challenge Dataset that contains images with low overlap with each other.
Languageen
Tertiary TypeFull Paper
FormatDVD, On-line.
Size1209 KiB
Number of Files1
Target FileVIEW-CLUSTER_v21.pdf
Last Update2011:07.10.22.27.15 sid.inpe.br/banon/2001/03.30.15.38 banon
Metadata Last Update2011:07.23.15.36.12 sid.inpe.br/banon/2001/03.30.15.38 abrahmac@mail.usf.edu {D 2011}
Document Stagecompleted
Is the master or a copy?is the master
Mirrorsid.inpe.br/banon/2001/03.30.15.38.24
e-Mail Addressabrahmac@mail.usf.edu
User Groupabrahmac@mail.usf.edu banon
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
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History2011-07-20 23:25:56 :: abrahmac@mail.usf.edu -> banon :: 2011
2011-07-20 23:33:26 :: banon -> abrahmac@mail.usf.edu :: 2011
2011-07-23 15:36:12 :: abrahmac@mail.usf.edu -> :: 2011
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
Access Date2019, Dec. 09

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