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.}
}