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SERP: SURF Enhancer for Repeated Pattern

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6939))

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Abstract

This paper proposes an object-matching method for repetitive patterns. Mismatching problems occur when descriptor-based features like SURF or SIFT are applied to repeated image patterns due to the use of the usual distance-ratio test. To overcome this, we first classify SURF descriptors in the image using mean-shift clustering. The repetitive features are grouped into a single cluster, and each non-repetitive feature has its own cluster. We then evaluate the similarity between the converged modes (descriptors) resulting from mean-shift clustering. We thus generate a new descriptor space that has a distinct and reliable descriptor for each cluster, and we use these to find correlations between images. We also calculate the homography between two images using the descriptors to guarantee correctness of the match. Experiments with repeated patterns show that this method improves recognition rates. This paper shows the results of applying this method to building recognition; the technique can be extended to matching various repeated patterns in textiles and geometric patterns.

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Computer Eng., Hanyang University, Korea

    Seung Jun Mok & Kyungboo Jung

  2. Dept. of Intelligent Robot Eng., Hanyang University, Korea

    Dong Wook Ko

  3. Dept. of Electrical Eng., BK21 Information Technology, Seoul National University, Korea

    Sang Hwa Lee

  4. Division of Computer Science & Eng., Hanyang University, Korea

    Byung-Uk Choi

Authors
  1. Seung Jun Mok
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  2. Kyungboo Jung
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  3. Dong Wook Ko
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  4. Sang Hwa Lee
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  5. Byung-Uk Choi
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

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

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, West Lafayette, 47907-2021, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana at Lafayette, 70504, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Mok, S.J., Jung, K., Ko, D.W., Lee, S.H., Choi, BU. (2011). SERP: SURF Enhancer for Repeated Pattern. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_58

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  • DOI: https://doi.org/10.1007/978-3-642-24031-7_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24030-0

  • Online ISBN: 978-3-642-24031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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