Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2012: Computer Vision - ACCV 2012 Workshops pp 26–37Cite as

3D LBP-Based Rotationally Invariant Region Description

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7728))

Abstract

Local binary patterns [LBP][1] are popular texture descriptors in many image analysis tasks. One of the important aspects of this texture descriptor is their rotational invariance. Most work in LBP has focused on 2D images. Here, we present a three dimensional LBP with a rotational invariant operator using spherical harmonics. Unlike Fehr and Burkhardt [2], the invariance is constructed implicitly, without considering all possible combinations of the pattern. We demonstrate the 3D LBP on phantom data and a clinical CTA dataset.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)

    Article  Google Scholar 

  2. Fehr, J., Burkhardt, H.: 3d rotation invariant local binary patterns. In: ICPR, pp. 1–4 (2008)

    Google Scholar 

  3. Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62, 61–81 (2005)

    Google Scholar 

  4. Tuceryan, M., Jain, A.K.: Handbook of pattern recognition & computer vision, pp. 235–276. World Scientific Publishing Co., Inc., River Edge (1993)

    Book  Google Scholar 

  5. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)

    Google Scholar 

  6. Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49, 117–125 (2010)

    Article  Google Scholar 

  7. Sørensen, L., Shaker, S.B., de Bruijne, M.: Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans. Med. Imaging 29, 559–569 (2010)

    Article  Google Scholar 

  8. Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.): Handbook of Pattern Recognition and Computer Vision, 2nd edn. World Scientific Publishing Co., Inc., River Edge (2000)

    Google Scholar 

  9. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)

    Article  Google Scholar 

  10. Jun, B., Kim, T., Kim, D.: A compact local binary pattern using maximization of mutual information for face analysis. Pattern Recognition 44, 532–543 (2011)

    Article  MATH  Google Scholar 

  11. Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.W.: Extended local binary patterns for texture classification. Image Vision Comput. 30, 86–99 (2012)

    Article  Google Scholar 

  12. Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing 18, 1107–1118 (2009)

    Article  Google Scholar 

  13. Guo, Z., Zhang, L., Zhang, D.: Rotation invariant texture classification using lbp variance (lbpv) with global matching. Pattern Recognition 43, 706–719 (2010)

    Article  MATH  Google Scholar 

  14. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19, 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  15. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1705–1720 (2010)

    Article  Google Scholar 

  16. Zhao, G., Ahonen, T., Matas, J., Pietikäinen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing 21, 1465–1477 (2012)

    Article  MathSciNet  Google Scholar 

  17. Green, R.: Spherical harmonic lighting: The gritty details. Archives of the Game Developers Conference (2003)

    Google Scholar 

  18. Kazhdan, M.M., Funkhouser, T.A., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: Symposium on Geometry Processing, pp. 156–165 (2003)

    Google Scholar 

  19. Fehr, J.: Local rotation invariant patch descriptors for 3d vector fields. In: ICPR, pp. 1381–1384 (2010)

    Google Scholar 

  20. Gluckman, J.: Kurtosis and the phase structure of images. In: 3rd International Workshop on Statistical and Computational Theories of Vision (in conjunction with ICCV 2003) (2003)

    Google Scholar 

  21. Cha, S.H.: Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences 1, 300–307 (2007)

    Google Scholar 

  22. Fuller, R.B.: U.s. patent no. 2,682,235 (building construction) (1954)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Radiology, Erasmus MC, Rotterdam, The Netherlands

    Jyotirmoy Banerjee, Adriaan Moelker, Wiro J. Niessen & Theo van Walsum

  2. Dept. of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

    Jyotirmoy Banerjee, Wiro J. Niessen & Theo van Walsum

Authors
  1. Jyotirmoy Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adriaan Moelker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wiro J. Niessen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Theo van Walsum
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hanyang University, 222 Wangshimni-ro, Seongdong-gu, 133-791, Seoul, South Korea

    Jong-Il Park

  2. Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, South Korea

    Junmo Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Banerjee, J., Moelker, A., Niessen, W.J., van Walsum, T. (2013). 3D LBP-Based Rotationally Invariant Region Description. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37410-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37409-8

  • Online ISBN: 978-3-642-37410-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation