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Feature Matching under Region-Based Constraints for Robust Epipolar Geometry Estimation

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5876))

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Abstract

Outlier-free inter-frame feature matches are important to accurate epipolar geometry estimation for many vision and robotics applications. We discover a set of high-level geometric and appearance constraints on low-level feature matches by exploiting reliable region matching results. A new outlier filtering scheme based on these constraints is proposed that can be combined with traditional robust statistical methods to identify outlier feature matches more reliably and efficiently. The proposed filtering scheme is tested in an real application of outdoor mobile robot navigation based on far-field scenes and of scenes that contain repeated structures.

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References

  1. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  2. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  3. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  4. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 1615–1630 (2005)

    Article  Google Scholar 

  5. Matas,J., et al.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. 9th European Conference on Computer Vision (ECCV 2002), vol. 1, pp. 384–393 (2002)

    Google Scholar 

  6. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  7. Torr, P.H.S., Murray, D.W.: The development and comparison of robust methods for estimating the fundamental matrix. International Journal of Computer Vision 24, 271–300 (1997)

    Article  Google Scholar 

  8. Zhang, Z.: Estimating motion and structure from correspondences of linesegments between two perspective images. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 1129–1139 (1995)

    Article  Google Scholar 

  9. Zhang, W., Kosecka, J.: A new inlier identification procedure for robust estimation problems. In: Proc. Robotics: Science and Systems Conference 2006, RSS 2006 (2006)

    Google Scholar 

  10. Bay, H., Ferrari, V., Van Gool, L.: Wide-baseline stereo matching with line segments. In: Proc. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2005), vol. 1, pp. 329–336 (2005)

    Google Scholar 

  11. Schaffalitzky, F., Zisserman, A.: Viewpoint invariant texture matching and wide baseline stereo. In: Proc. 18th IEEE International Conference on Computer Vision (ICCV 2001), pp. 636–643 (2001)

    Google Scholar 

  12. Ferrari, V.: Wide-baseline multiple-view correspondence. In: Proc. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2003), vol. 1, pp. 718–725 (2003)

    Google Scholar 

  13. Tao, H., Sawhney, H.S., Kumar, R.: A golbal matching framework for stereo computation. In: Proc. 18th IEEE International Conference on Computer Vision (ICCV 2001), pp. 532–539 (2001)

    Google Scholar 

  14. Toshev, A., Shi, J., Daniilidis, K.: Image matching via saliency region correspondences. In: Proc. 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2007), pp. 1–8 (2007)

    Google Scholar 

  15. Rehrmann, V., Priese, L.: Fast and robust segmentation of natural color scenes. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352, pp. 598–606. Springer, Heidelberg (1998)

    Google Scholar 

  16. Ross, M.: Segment clustering tracking. In: Proc. 2nd Europ. Conf. on Colour in Graphics, Imaging, and Visualization, pp. 598–606 (2004)

    Google Scholar 

  17. Rehrmann, V.: Object oriented motion estimation in color image sequences. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 704–719. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  18. Hornberg, A.: Handbook of Machine Vision. Wiley, Chichester (2006)

    Book  Google Scholar 

  19. Gonzalez, R.C., Woods, R., Eddins, S.L.: Digital Image Processing Using MATLAB. Prentice Hall, Englewood Cliffs (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Colorado at Boulder, Boulder, Colorado, 80309-0430, USA

    Wei Xu & Jane Mulligan

Authors
  1. Wei Xu
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  2. Jane Mulligan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Xu, W., Mulligan, J. (2009). Feature Matching under Region-Based Constraints for Robust Epipolar Geometry Estimation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_24

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  • DOI: https://doi.org/10.1007/978-3-642-10520-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10519-7

  • Online ISBN: 978-3-642-10520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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