Skip to main content

Accurate Image Matching in Scenes Including Repetitive Patterns

  • Conference paper
Book cover Robot Vision (RobVis 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4931))

Included in the following conference series:

Abstract

We propose an accurate method for image matching in scenes including repetitive patterns like buildings, walls, and so on. We construct our matching method with two phases: matching between the elements of repetitive regions; matching between the points in the remained regions. We first detect the elements of repetitive patterns in each image and find matches between the elements in the regions without using any descriptors depended on a view-point. We then find matches between the points in the remained regions of the two images using the informations of the detected matches. The advantage of our method is to use an efficient matching information in the repetitive patterns. We show the effectiveness of our method by real image examples.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., Manchester, U.K, pp. 147–151 (1988)

    Google Scholar 

  2. Kanazawa, Y., Kanatani, K.: Robust image matching preserving global consistency. In: Proc. 6th Asian Conf. Comput, Jeju Island, Korea, pp. 1128–1133 (2004)

    Google Scholar 

  3. Lowe, D.: Distinctive image features from scale-invariant keypoint. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  4. Matas, J., et al.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. 13th British Machine Vision Conf., Cardiff, U.K, pp. 384–393 (2002)

    Google Scholar 

  5. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detector. Int. J. Comput. Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

  6. Mikolajczyk, K., et al.: A comparizon of affine region detectors. Int. J. Comput. Vision 65(1–2), 43–72 (2005)

    Article  Google Scholar 

  7. Kanazawa, Y., Uemura, K.: Wide baseline matching using triplet vector descriptor. In: Proc. 17th British Machine Vision Conf., Edinburgh, U.K, vol. 1, pp. 267–276 (2006)

    Google Scholar 

  8. Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Patt. Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  10. Roerdink, M.: The watershed transform: Definitions, algorithms and parallelization strategies. FUNDINF: Fundamenta Informatica 41 (2000)

    Google Scholar 

  11. Hartley, R., Zisserman, A.: Multiple View Geometry. Cambridge University press, Cambridge (2000)

    MATH  Google Scholar 

  12. Kanatani, K., Ohta, N., Kanazawa, Y.: Optimal homography computation with a reliability measure. IEICE Trans. Inf. & Syst. E83-D(7), 1369–1374 (2000)

    Google Scholar 

  13. Kanazawa, Y., Sakamoto, T., Kawakami, H.: Robust 3-d reconstruction using one or more homographies with uncalibrated stereo. In: Proc. 6th Asian Conf. Comput. Vision, Jeju Island, Korea, pp. 503–508 (2004)

    Google Scholar 

  14. Kanatani, K.: Optimal fundamental matrix computation: algorithm and reliability analysis. In: Proc. 6th Symposium on Sensing via Imaging Information (SSII), Yokohama, Japan, pp. 291–296 (2000)

    Google Scholar 

  15. Sugaya, Y., Kanatani, K., Kanazawa, Y.: Generating dense point matches using epipolar geometry. Memoirs of the Faculty of Engineering, Okayama University 40, 44–57 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Gerald Sommer Reinhard Klette

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kamiya, S., Kanazawa, Y. (2008). Accurate Image Matching in Scenes Including Repetitive Patterns. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78157-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78156-1

  • Online ISBN: 978-3-540-78157-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics