Processing Geotagged Image Sets for Collaborative Compositing and View Construction (bibtex)
by Levente Kovács
Abstract:
In this paper we present a method for local processing of photos and associated sensor information on mobile devices. Our goal is to lay the foundations of a collaborative multi-user framework where ad-hoc device groups can share their data around a geographical location to produce more complex composited views of the area, without the need of a centralized server-client - cloud-based - architecture. We focus on processing as much data locally on the devices as possible, and reducing the amount of data that needs to be shared. The main results are the proposal of a lightweight processing and feature extraction framework, based on the analysis of vision graphs, and presenting preliminary composite view generation based on these results.
Reference:
Levente Kovács, Processing Geotagged Image Sets for Collaborative Compositing and View Construction, In Proceedings of ICCV Workshop on Computer Vision for Converging Perspectives, Sydney, Australia, pp. 460-467, 2013.
Bibtex Entry:
@string{iccv-cvcp="Proceedings of ICCV Workshop on Computer Vision for Converging Perspectives"}
@InProceedings{LKovacs2013a,
  author =	 {Kov\'acs, Levente},
  title =	 {Processing Geotagged Image Sets for Collaborative Compositing and View Construction},
  booktitle =	 iccv-cvcp,
  pages =	 {460-467},
  year =	 2013,
  address =	 {Sydney, Australia},
  month =	 dec,
  organization = {IEEE},
  pdf = {http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W17/papers/Kovacs_Processing_Geotagged_Image_2013_ICCV_paper.pdf},
  abstract =	 {In this paper we present a method for local processing
                of photos and associated sensor information on mobile devices.
                Our goal is to lay the foundations of a collaborative
                multi-user framework where ad-hoc device groups can
                share their data around a geographical location to produce
                more complex composited views of the area, without the
                need of a centralized server-client - cloud-based - architecture.
                We focus on processing as much data locally on the
                devices as possible, and reducing the amount of data that
                needs to be shared. The main results are the proposal of
                a lightweight processing and feature extraction framework,
                based on the analysis of vision graphs, and presenting preliminary
                composite view generation based on these results.}
}
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