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@InProceedings{BrahmachariSark:2011:ViClWi,
               author = "Brahmachari, Aveek Shankar and Sarkar, Sudeep",
          affiliation = "University of South Florida, Computer Science and Engineering and 
                         University of South Florida, Computer Science and Engineering",
                title = "View Clustering of Wide-Baseline N-Views for Photo Tourism",
            booktitle = "Proceedings...",
                 year = "2011",
               editor = "Lewiner, Thomas and Torres, Ricardo",
         organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
            publisher = "IEEE Computer Society Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Computer Vision, Image Collection, Epipolar Geometry, Photo 
                         Organization.",
             abstract = "The 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.",
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "VIEW-CLUSTER_v21.pdf",
        urlaccessdate = "2019, Dec. 09"
}


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