Detecting and matching repeated patterns for automatic geo-tagging in urban environments

Grant Schindler; Panchapagesan Krishnamurthy; Roberto Lublinerman; Yanxi Liu; Frank Dellaert

doi:10.1109/CVPR.2008.4587461

Detecting and matching repeated patterns for automatic geo-tagging in urban environments

Grant Schindler, Panchapagesan Krishnamurthy, Roberto Lublinerman, Yanxi Liu, Frank Dellaert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

111 Scopus citations

Abstract

We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

Original language	English (US)
Title of host publication	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs	https://doi.org/10.1109/CVPR.2008.4587461
State	Published - 2008
Event	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States Duration: Jun 23 2008 → Jun 28 2008

Publication series

Name	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Country/Territory	United States
City	Anchorage, AK
Period	6/23/08 → 6/28/08

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Control and Systems Engineering

Access to Document

10.1109/CVPR.2008.4587461

Cite this

Schindler, G., Krishnamurthy, P., Lublinerman, R., Liu, Y., & Dellaert, F. (2008). Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR Article 4587461 (26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR). https://doi.org/10.1109/CVPR.2008.4587461

@inproceedings{149f7565413b4c9e8f11d9abad62d7bb,

title = "Detecting and matching repeated patterns for automatic geo-tagging in urban environments",

abstract = "We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.",

author = "Grant Schindler and Panchapagesan Krishnamurthy and Roberto Lublinerman and Yanxi Liu and Frank Dellaert",

year = "2008",

doi = "10.1109/CVPR.2008.4587461",

language = "English (US)",

isbn = "9781424422432",

series = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR",

booktitle = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR",

note = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR ; Conference date: 23-06-2008 Through 28-06-2008",

}

Schindler, G, Krishnamurthy, P, Lublinerman, R, Liu, Y & Dellaert, F 2008, Detecting and matching repeated patterns for automatic geo-tagging in urban environments. in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR., 4587461, 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Anchorage, AK, United States, 6/23/08. https://doi.org/10.1109/CVPR.2008.4587461

Detecting and matching repeated patterns for automatic geo-tagging in urban environments. / Schindler, Grant; Krishnamurthy, Panchapagesan; Lublinerman, Roberto et al.
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. 2008. 4587461 (26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detecting and matching repeated patterns for automatic geo-tagging in urban environments

AU - Schindler, Grant

AU - Krishnamurthy, Panchapagesan

AU - Lublinerman, Roberto

AU - Liu, Yanxi

AU - Dellaert, Frank

PY - 2008

Y1 - 2008

N2 - We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

AB - We present a novel method for automatically geo-tagging photographs of man-made environments via detection and matching of repeated patterns. Highly repetitive environments introduce numerous correspondence ambiguities and are problematic for traditional wide-baseline matching methods. Our method exploits the highly repetitive nature of urban environments, detecting multiple perspectively distorted periodic 2D patterns in an image and matching them to a 3D database of textured facades by reasoning about the underlying canonical forms of each pattern. Multiple 2D-to-3D pattern correspondences enable robust recovery of camera orientation and location. We demonstrate the success of this method in a large urban environment.

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DO - 10.1109/CVPR.2008.4587461

M3 - Conference contribution

AN - SCOPUS:51949108641

SN - 9781424422432

T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Y2 - 23 June 2008 through 28 June 2008

ER -

Detecting and matching repeated patterns for automatic geo-tagging in urban environments

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