More Web Proxy on the site http://driver.im/

poster

Evaluation of feature detectors for registering aerial images

Authors:

Andrew Bainbridge-SmithAuthors Info & Claims

IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand

Pages 192 - 197

https://doi.org/10.1145/2425836.2425877

Published: 26 November 2012 Publication History

Abstract

The detection, extraction, and matching of image features is a popular method for generating point-to-point correspondences for the estimation of scene and camera geometries. In this work we evaluate the performance of a variety of feature detection algorithms over two reference data sets and a set of aerial images which includes large changes in scene illumination. The evaluated detectors showed expected performance against the reference data sets, and aerial images with constant lighting conditions, but were unsuccessful in aligning image pairs showing strong changes in image exposure and illumination.

References

[1]

M. Agrawal, K. Konolige, and M. R. Blas. CenSurE: center surround extremas for realtime feature detection and matching. In D. Forsyth, P. Torr, and A. Zisserman, editors, Computer Vision -- ECCV 2008, volume 5305, pages 102--115. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.

[2]

H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3): 346--359, 2008.

Digital Library

[3]

T. Botterill. Visual Navigation for Mobile Robots using the Bag-of-Words Algorithm. PhD thesis, University of Canterbury, Christchurch, New Zealand, 2010.

[4]

T. Botterill, S. Mills, and R. Green. New conditional sampling strategies for speeded-up RANSAC. In Proceedings of the British Machine Vision Conference, 2009.

[5]

O. Chum and J. Matas. Matching with PROSAC -- progressive sample consensus. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 220--226, San Diego, CA, USA.

Digital Library

[6]

K. Cordes, B. Rosenhahn, and J. Ostermann. Increasing the accuracy of feature evaluation benchmarks using differential evolution. In Differential Evolution (SDE), 2011 IEEE Symposium on, pages 1--8. IEEE, Apr. 2011.

[7]

C. Engels, H. Stewénius, and D. Nistér. Bundle adjustment rules. Photogrammetric Computer Vision, 2, 2006.

[8]

C. Evans. Notes on the OpenSURF library. Technical Report CSTR-09-001, University of Bristol, Jan. 2009.

[9]

S. Gauglitz, T. Höllerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3): 335--360, Mar. 2011.

Digital Library

[10]

J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts. Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(12): 1338--1350, Dec. 2001.

Digital Library

[11]

C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, page 50, 1988.

[12]

R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition, 2004.

Digital Library

[13]

R. Hess. An open-source SIFT Library. In Proceedings of the international conference on Multimedia, pages 1493--1496, 2010.

Digital Library

[14]

D. G. Lowe. Distinctive image features from Scale-Invariant keypoints. International Journal of Computer Vision, 60(2): 91--110, Nov. 2004.

Digital Library

[15]

K. Mikolajczyk and C. Schmid. An affine invariant interest point detector. In A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, editors, Computer Vision -- ECCV 2002, volume 2350, pages 128--142. Springer Berlin Heidelberg, Berlin, Heidelberg.

Digital Library

[16]

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool. A comparison of affine region detectors. International Journal of Computer Vision, 65(--2): 43--72, Oct. 2005.

Digital Library

[17]

OpenCV development team. OpenCV v2.3 documentation. Technical report, Aug. 2011.

[18]

E. Rosten and T. Drummond. Machine learning for High-Speed corner detection. In A. Leonardis, H. Bischof, and A. Pinz, editors, Computer Vision -- ECCV 2006, volume 3951, pages 430--443. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.

Digital Library

[19]

E. Rosten, R. Porter, and T. Drummond. Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1): 105--119, Jan. 2010.

Digital Library

[20]

A. Schmidt, M. Kraft, and A. Kasiński. An evaluation of image feature detectors and descriptors for robot navigation. In L. Bolc, R. Tadeusiewicz, L. J. Chmielewski, and K. Wojciechowski, editors, Computer Vision and Graphics, volume 6375, pages 251--259. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010.

Digital Library

[21]

J. Shi and C. Tomasi. Good features to track. In Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, pages 593--600. IEEE Comput. Soc. Press, 1994.

[22]

C. Tomasi and T. Kanade. Detection and tracking of point features, 1991.

[23]

P. Torr. MLESAC: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1): 138--156, Apr. 2000.

Digital Library

[24]

T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3): 177--280, 2007.

Digital Library

Cited By

Wang VHayes M(2017)Synthetic Aperture Sonar Track Registration Using SIFT Image CorrespondencesIEEE Journal of Oceanic Engineering10.1109/JOE.2016.263407842:4(901-913)Online publication date: Oct-2017
https://doi.org/10.1109/JOE.2016.2634078
Marburg AHayes MBainbridge-Smith A(2013)Pose Priors for Aerial Image Registration2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA.2013.6691515(1-8)Online publication date: Nov-2013
https://doi.org/10.1109/DICTA.2013.6691515

Index Terms

Evaluation of feature detectors for registering aerial images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Computer graphics
    1. Image manipulation

Recommendations

Pose Guided Matching for Aerial Images
IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand

The estimation of the geometric relationship between pairs of images is a core task for many computer vision applications. Frequently, prior information on the inter-image or inter-camera geometry is available from e.g., a motion model or external ...
Design and evaluation of feature detectors
Feature Detection and Description in Remote Sensing Images using a Hybrid Feature Detector
Abstract
Feature detection plays a crucial role in image registration. There exists quite a few feature detection algorithms in literature like BRISK, FAST, SURF etc [14]. Each of these algorithms has its own advantages and disadvantages. BRISK is rotation ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

IVCNZ '12: Proceedings of the 27th Conference on Image and Vision Computing New Zealand

November 2012

547 pages

ISBN:9781450314732

DOI:10.1145/2425836

Editors:
Brendan McCane
University of Otago
,
Steven Mills
University of Otago
,
Jeremiah Deng
University of Otago

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

HRS: Hoare Research Software Ltd.
Google Inc.
Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

IVCNZ '12

Sponsor:

HRS
Dept. of Information Science, Univ.of Otago

IVCNZ '12: Image and Vision Computing New Zealand

November 26 - 28, 2012

Dunedin, New Zealand

Acceptance Rates

Overall Acceptance Rate 55 of 74 submissions, 74%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
112
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang VHayes M(2017)Synthetic Aperture Sonar Track Registration Using SIFT Image CorrespondencesIEEE Journal of Oceanic Engineering10.1109/JOE.2016.263407842:4(901-913)Online publication date: Oct-2017
https://doi.org/10.1109/JOE.2016.2634078
Marburg AHayes MBainbridge-Smith A(2013)Pose Priors for Aerial Image Registration2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA.2013.6691515(1-8)Online publication date: Nov-2013
https://doi.org/10.1109/DICTA.2013.6691515

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents