Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-11301-7_71
DC FieldValue
dc.titleEstimating poses of world's photos with geographic metadata
dc.contributor.authorLuo, Z.
dc.contributor.authorLi, H.
dc.contributor.authorTang, J.
dc.contributor.authorHong, R.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-04T08:14:30Z
dc.date.available2013-07-04T08:14:30Z
dc.date.issued2009
dc.identifier.citationLuo, Z.,Li, H.,Tang, J.,Hong, R.,Chua, T.-S. (2009). Estimating poses of world's photos with geographic metadata. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5916 LNCS : 695-700. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-11301-7_71" target="_blank">https://doi.org/10.1007/978-3-642-11301-7_71</a>
dc.identifier.isbn3642113001
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40880
dc.description.abstractUsers can explore the world by viewing place related photos on Google Maps. One possible way is to take the nearby photos for viewing. However, for a given geo-location, many photos with view directions not pointing to the desired regions are returned by that world map. To address this problem, prior know the poses in terms of position and view direction of photos is a feasible solution. We can let the system return only nearby photos with view direction pointing to the target place, to facilitate the exploration of the place for users. Photo's view direction can be easily obtained if the extrinsic parameters of its corresponding camera are well estimated. Unfortunately, directly employing conventional methods for that is unfeasible since photos fallen into a range of certain radius centered at a place are observed be largely diverse in both content and view. Int this paper, we present a novel method to estimate the view directions of world's photos well. Then further obtain the pose referenced on Google Maps using the geographic Metadata of photos. The key point of our method is first generating a set of subsets when facing a large number of photos nearby a place, then reconstructing the scenes expressed by those subsets using normalized 8-point algorithm. We embed a search based strategy with scene alignment to product those subsets. We evaluate our method by user study on an online application developed by us, and the results show the effectiveness of our method. © 2010 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-11301-7_71
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-11301-7_71
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5916 LNCS
dc.description.page695-700
dc.identifier.isiutNOT_IN_WOS
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