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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3L7LB
Repositorysid.inpe.br/sibgrapi/2011/07.10.22.27
Last Update2011:07.10.22.27.15 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.10.22.27.15
Metadata Last Update2022:06.14.00.07.16 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.43
Citation KeyBrahmachariSark:2011:ViClWi
TitleView Clustering of Wide-Baseline N-Views for Photo Tourism
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 29
Number of Files1
Size1209 KiB
2. Context
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
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addressabrahmac@mail.usf.edu
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-20 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 -> administrator :: 2011
2022-06-14 00:07:16 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > View Clustering of...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > View Clustering of...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3L7LB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3L7LB
Languageen
Target FileVIEW-CLUSTER_v21.pdf
User Groupabrahmac@mail.usf.edu
banon
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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