Skip to main content
SpringerLink
Log in

A View Based Approach for Matching the 3D Appearance of Local Features

  • Conference paper
Image Analysis and Recognition (ICIAR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7950))

Included in the following conference series:

  • 5051 Accesses

Abstract

This paper presents a novel 3D RGB-D keypoint extraction approach for creating stable point-to-point correspondences between 3D free form objects under changes in view point, termed view compartmentalised keypoints or VC keypoints. VC Keypoints are extracted and matched based on the local directionally dependant appearances of the keypoints from a gamut of observed 3D poses, creating a 3D structure of keypoint appearance across the view space. In addition, we show how keypoints observed across a range of views can be integrated to form more comprehensive keypoint descriptors. The VC keypoint methodology shows an improvement in point-to-point matching over SIFT, SURF and EC-SIFT.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)

    Article  Google Scholar 

  2. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)

    Article  Google Scholar 

  3. Calonder, M., Lepetit, V., Fua, P.: Keypoint signatures for fast learning and recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 58–71. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press (2005)

    Google Scholar 

  5. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In: Khatib, O., Kumar, V., Sukhatme, G. (eds.) Experimental Robotics. STAR, vol. 79, pp. 477–491. Springer, Heidelberg (2012)

    Google Scholar 

  6. Jiantao, P., Yi, L., Guyu, X., Hongbin, Z., Weibin, L., Uehara, Y.: 3d model retrieval based on 2d slice similarity measurements. In: Proceedings of 2nd International Symposium on 3D Data Processing, Visualization and Transmission, pp. 95–101. IEEE (2004)

    Google Scholar 

  7. Lo, T.W.R., Siebert, J.P.: Local feature extraction and matching on range images: 2.5 d sift. Computer Vision and Image Understanding 113(12), 1235–1250 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Ohbuchi, R., Furuya, T.: Scale-weighted dense bag of visual features for 3d model retrieval from a partial view 3d model. In: IEEE Twelfth International Conference on Computer Vision Workshops, pp. 63–70. IEEE (2009)

    Google Scholar 

  12. Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3d model retrieval. In: IEEE International Conference on Shape Modeling and Applications, SMI 2008, pp. 93–102. IEEE (2008)

    Google Scholar 

  13. Pears, N., Heseltine, T., Romero, M.: From 3d point clouds to pose-normalised depth maps. International Journal of Computer vision 89(2), 152–176 (2010)

    Article  Google Scholar 

  14. Peters, G., Zitova, B., Von der Malsburg, C.: How to measure the pose robustness of object views. Image and Vision Computing 20(4), 249–256 (2002)

    Article  Google Scholar 

  15. Strachan, E., Siebert, J.P.: Local multi-modal sift features in co-registered range and intensity images. In: International Conference on Signal Image Technology and Internet Based Systems, pp. 426–432. IEEE (2012)

    Google Scholar 

  16. Taati, B., Greenspan, M.: Local shape descriptor selection for object recognition in range data. Computer Vision and Image Understanding 115(5), 681–694 (2011)

    Article  Google Scholar 

  17. Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Twenty-First Annual Conference on Computer Graphics and Interactive Techniques, pp. 311–318. ACM (1994)

    Google Scholar 

  18. Winder, S., Hua, G., Brown, M.: Picking the best daisy. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 178–185. IEEE (2009)

    Google Scholar 

  19. Simon, A.J.: Winder and Matthew Brown. Learning local image descriptors. In: Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Glasgow, Glasgow, UK

    Euan Strachan & J. Paul Siebert

Authors
  1. Euan Strachan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Paul Siebert
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Strachan, E., Siebert, J.P. (2013). A View Based Approach for Matching the 3D Appearance of Local Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39094-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39093-7

  • Online ISBN: 978-3-642-39094-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation