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Localizing
Unordered Panoramic Images Using the Levenshtein Distance |
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Brief description |
There is currently an abundance of vision algorithms which, provided
with a sequence of images that have been acquired from sufficiently close
successive 3D locations, are capable of determining the relative positions of
the viewpoints from which the images have been captured. However, very few of
these algorithms can cope with unordered image sets. We present an efficient
method for recovering the position and orientation parameters corresponding
to the viewpoints of a set of panoramic images for which no a priori order
information is available, along with certain structure information regarding
the imaged environment. The proposed approach operates sequentially,
employing the Levenshtein distance to deduce the spatial proximity of image
viewpoints and thus determine the order in which images should be processed.
The Levenshtein distance also provides matches between imaged points, from
which their corresponding environment points can be reconstructed. Image
matching with the aid of the Levenshtein distance forms the crux of an
iterative process that alternates between image localization from multiple
reconstructed points and point reconstruction from multiple image
projections, until all views have been localized. Periodic refinement of the
reconstruction with the aid of bundle adjustment, distributes the
reconstruction error among images. The approach is demonstrated on several
unordered sets of panoramic images obtained in indoor environments. |
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Contributions |
Damien Michel, Antonis Argyros, |
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Sample results |
Example of the application of the proposed method
on the problem of simultaneous robot mapping and localization. Note that the
order of images is not provided to the algorithm, but inferred by the
proposed algorithm itself.
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Related publications and documents |
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D. Michel, A.A. Argyros and M.I.A. Lourakis, "Localizing Unordered
Panoramic Images Using the Levenshtein Distance", Proc. of 7th workshop
on Omnidirectional Vision, Camera Networks and Non-classical Cameras
(OMNIVIS'2007), in conjunction with ICCV’07, Rio de Janeiro, Brazil,
2007 |
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The electronic versions of the above publications can be downloaded
from my publications
page. |
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Last update: |
05 April 2008,
Antonis Argyros, argyros@ics.forth.gr |
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