Keypoints Detection in RGB-D Space - A Hybrid Approach

Nizar Sallem, Michel Devy, Radu Rusu, Suat Gedikili

Abstract

Features detection is an important technique of image processing which aim is to find a subset, often discrete, of a query image satisfying uniqueness and discrimination criteria so that an image can be abstracted to the computed features. Detected features are then used in video indexing, registration, object and scene reconstruction, structure from motion, etc. In this article we discuss the definition and implementation of such features in the RGB-Depth space RGB-D.We focus on the corners as they are the most used features in image processing. We show the advantage of using 3D data over image only techniques and the power of combining geometric and colorimetric information to find corners in a scene.

References

  1. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. pages 404-417.
  2. Harris, C. and Stephens, M. (1988). A combined corner and edge detection. In Proceedings of The Fourth Alvey Vision Conference, pages 147-151.
  3. Knopp, J., Prasad, M., Willems, G., Timofte, R., and Van Gool, L. (2010). Hough transform and 3d surf for robust three dimensional classification. In Proceedings of the 11th European conference on Computer vision: Part VI, ECCV'10, pages 589-602, Berlin, Heidelberg. Springer-Verlag.
  4. Lai, K., Bo, L., Ren, X., and Fox, D. (2011). A largescale hierarchical multi-view RGB-D object dataset. In ICRA, pages 1817-1824. IEEE.
  5. Li, J. and Allinson, N. M. (2008). A comprehensive review of current local features for computer vision. Neurocomput., 71(10-12):1771-1787.
  6. Moravec, H. P. (1981). 3d graphics and the wave theory. In SIGGRAPH 7881: Proceedings of the 8th annual conference on Computer graphics and interactive techniques, pages 289-296, New York, NY, USA. ACM.
  7. Redondo-Cabrera, C., Lopez-Sastre, R. J., AcevedoRodriguez, J., and Maldonado-Bascon, S. (2012). SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition. In IEEE CVPR.
  8. Sipiran, I. and Bustos, B. (2011). Harris 3d: a robust extension of the harris operator for interest point detection on 3d meshes. Vis. Comput., 27(11):963-976.
  9. Smith, S. M. and Brady, J. M. (1995). Susan - a new approach to low level image processing. International Journal of Computer Vision, 23:45-78.
  10. Trajkovic, M. and Hedley, M. (1998). Fast corner detection. Image Vision Comput., 16(2):75-87.
  11. Tuytelaars, T. and Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis., 3(3):177-280.
Download


Paper Citation


in Harvard Style

Sallem N., Devy M., Gedikili S. and Rusu R. (2013). Keypoints Detection in RGB-D Space - A Hybrid Approach . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 496-499. DOI: 10.5220/0004305004960499


in Bibtex Style

@conference{visapp13,
author={Nizar Sallem and Michel Devy and Suat Gedikili and Radu Rusu},
title={Keypoints Detection in RGB-D Space - A Hybrid Approach},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={496-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004305004960499},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Keypoints Detection in RGB-D Space - A Hybrid Approach
SN - 978-989-8565-47-1
AU - Sallem N.
AU - Devy M.
AU - Gedikili S.
AU - Rusu R.
PY - 2013
SP - 496
EP - 499
DO - 10.5220/0004305004960499