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Evaluation of Image Feature Descriptors for Marker-Less AR Applications

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8888))

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Abstract

For employing marker-less augmented reality (AR), image-based geometric alignment is one of the fundamental functions. Image feature descriptor is widely used for this purpose. In this paper, we evaluate various image feature descriptors for vision-based marker-less AR applications. To evaluate descriptors in a case where occlusion exists, we use not only 2D image test data but also images generated by 3D computer graphic models. We conducted experiments evaluating performance of detection, matching, and tracking, and compared them.

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References

  1. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: Fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)

    Google Scholar 

  2. Ambai, M., Yoshida, Y.: CARD: Compact and real-time descriptors. In: IEEE International Conference on Computer Vision, pp. 97–104 (2011)

    Google Scholar 

  3. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Fraundorfer, F., Bischof, H.: Evaluation of local detectors on non-planar scenes. In: Proceedings of the Austrian Association for Pattern Recognition Workshop, pp. 125–132 (2004)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  7. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–513 (2004)

    Google Scholar 

  8. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (November 2007)

    Google Scholar 

  9. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  10. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions of Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  11. Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. International Jounal of Computer Vision 73, 263–284 (2007)

    Article  Google Scholar 

  12. Morel, J.M., Yu, G.: “asift: A new framework for fully affine invariant image comparison”. SIAM Journal on Imaging Sciences, 438–469 (2009)

    Google Scholar 

  13. Özuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Transactions on Pettern Analysis and Machine Intelligence 32(3), 448–461 (2010)

    Article  Google Scholar 

  14. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to sift or surf. In: Proc. International Conference on Computer VIsion, pp. 2564–2571 (2011)

    Google Scholar 

  15. Schmid, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  16. Stein, A., Herbert, M.: Incorporating background invariance into feature-based object recognition. In: Workshop on Applications of Computer Vision, vol. 1, pp. 37–44 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Tokyo University of Agriculture and Technology, Nakacho 2-24-16, Koganei, Tokyo, Japan

    Hiroshi Koyasu

  2. Saitama University, ShimoOkubo 255, Saitama, Japan

    Kotaro Nozaki & Hitoshi Maekawa

Authors
  1. Hiroshi Koyasu
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  2. Kotaro Nozaki
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  3. Hitoshi Maekawa
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at 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. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Sata Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Koyasu, H., Nozaki, K., Maekawa, H. (2014). Evaluation of Image Feature Descriptors for Marker-Less AR Applications. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-14364-4_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14363-7

  • Online ISBN: 978-3-319-14364-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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