Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-11301-7_71
Title: Estimating poses of world's photos with geographic metadata
Authors: Luo, Z.
Li, H. 
Tang, J. 
Hong, R. 
Chua, T.-S. 
Issue Date: 2009
Source: Luo, 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. https://doi.org/10.1007/978-3-642-11301-7_71
Abstract: Users 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.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40880
ISBN: 3642113001
ISSN: 03029743
DOI: 10.1007/978-3-642-11301-7_71
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