Robust 3-D Object Skeletonisation for the Similarity Measure

Christian Feinen, David Barnowsky, Dietrich Paulus, Marcin Grzegorzek

Abstract

In this paper we introduce our approach for similarity measure of 3-D objects based on an existing curve skeletonization technique. This skeletonization algorithm for 3-D objects delivers skeletons thicker than 1 voxel. This makes an efficient distance or similarity measure impossible. To overcome this drawback, we use a significantly extended skeletonization algorithm (by Reniers) and a modified Dijkstra approach. In addition to that, we propose features that are directly extracted from the resulting skeletal structures. To evaluate our system, we created a ground truth of 3-D objects and their similarities estimated by humans. The automatic similarity results achieved by our system were evaluated against this ground truth in terms of precision and recall in an object retrieval setup.

References

  1. Bai, X. and Latecki, L. (2008). Path similarity skeleton graph matching. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(7):1282-1292.
  2. Blum, H. (1967). A transformation for extracting new descriptors of shape. In Wathen-Dunn, W., editor, Models for the Perception of Speech and Visual Form, pages 362-380. MIT Press, Cambridge.
  3. Borgefors, G. (1996). On digital distance transforms in three dimensions. Computer Vision and Image Understanding, 64(3):368 - 376.
  4. Cao, J., Tagliasacchi, A., Olson, M., Zhang, H., and Su, Z. (2010). Point cloud skeletons via laplacian based contraction. In Shape Modeling International Conference (SMI), 2010, pages 187-197.
  5. Chang, S. (2007). Extracting skeletons from distance map. International Journal of Computer Science and Network Security, 7(7):213-219.
  6. Cornea, N., Demirci, M., Silver, D., Shokoufandeh, Dickinson, S., and Kantor, P. (2005). 3d object retrieval using many-to-many matching of curve skeletons. In Shape Modeling and Applications, 2005 International Conference, pages 366-371.
  7. Fabbri, R., Costa, L. D. F., Torelli, J. C., and Bruno, O. M. (2008). 2d euclidean distance transform algorithms: A comparative survey. ACM Comput. Surv., 40(1):1-44.
  8. Hassouna, M. and Farag, A. (2007). On the extraction of curve skeletons using gradient vector flow. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8.
  9. Hayashi, T., Raynal, B., Nozick, V., and Saito, H. (2011). Skeleton features distribution for 3d object retrieval. In proc. of the 12th IAPR Machine Vision and Applications (MVA2011), pages 377-380, Nara, Japan.
  10. Ma, C. M. and Sonka, M. (1996). A fully parallel 3d thinning algorithm and its applications. Comput. Vis. Image Underst., 64(3):420-433.
  11. Macrini, D., Shokoufandeh, A., Dickinson, S., Siddiqi, K., and Zucker, S. (2002). View-based 3-d object recognition using shock graphs. In Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3, ICPR 7802, pages 24- 28, Washington, DC, USA. IEEE Computer Society.
  12. Ogniewicz, R. and Ilg, M. (1992). Voronoi skeletons: theory and applications. In Computer Vision and Pattern Recognition, 1992. Proceedings CVPR 7892., 1992 IEEE Computer Society Conference on, pages 63-69.
  13. Ogniewicz, R. L. and Kübler, O. (1995). Hierarchic voronoi skeletons. Pattern Recognition, Vol. 28, pages 343- 359.
  14. Palágyi, K. and Kuba, A. (1998). A 3d 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognition Letters 19, pages 613-627.
  15. Palágyi, K. and Kuba, A. (1999). Directional 3d thinning using 8 subiterations. In Proceedings of the 8th International Conference on Discrete Geometry for Computer Imagery, DCGI 7899, pages 325-336, London, UK, UK. Springer-Verlag.
  16. Pizlo, Z. (2008). 3D Shape: Its Unique Place in Visual Perception. The MIT Press, Cambridge - Massachusetts, London - England.
  17. Reniers, D. (2008). Skeletonization and Segmentation of Binary Voxel Shapes. PhD thesis, Technische Universiteit Eindhoven.
  18. Reniers, D. and Telea, A. (2006). Quantitative comparison of tolerance-based feature transforms. First International Conference on Computer Vision Theory and Applications (VISAPP), pages 107-114.
  19. Schäfer, S. (2011). Path similarity skeleton graph matching for 3d objects. Master's thesis, Universität KoblenzLandau.
  20. Sharf, A., Lewiner, T., Shamir, A., and Kobbelt, L. (2007). On-the-fly Curve-skeleton Computation for 3D Shapes. Computer Graphics Forum, 26(3):323- 328.
  21. Siddiqi, K. and Pizer, S. (2008). Medial Representations: Mathematics, Algorithms and Applications. Springer Publishing Company, Incorporated, 1st edition.
  22. Sundar, H., Silver, D., Gagvani, N., and Dickinson, S. (2003). Skeleton based shape matching and retrieval. In Shape Modeling International, 2003, pages 130- 139.
  23. Zhang, J., Siddiqi, K., Macrini, D., Shokoufandeh, A., and Dickinson, S. (2005). Retrieving articulated 3- d models using medial surfaces and their graph spectra. In Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'05, pages 285- 300, Berlin, Heidelberg. Springer-Verlag.
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Paper Citation


in Harvard Style

Feinen C., Barnowsky D., Paulus D. and Grzegorzek M. (2013). Robust 3-D Object Skeletonisation for the Similarity Measure . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 167-175. DOI: 10.5220/0004255601670175


in Bibtex Style

@conference{icpram13,
author={Christian Feinen and David Barnowsky and Dietrich Paulus and Marcin Grzegorzek},
title={Robust 3-D Object Skeletonisation for the Similarity Measure},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={167-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004255601670175},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Robust 3-D Object Skeletonisation for the Similarity Measure
SN - 978-989-8565-41-9
AU - Feinen C.
AU - Barnowsky D.
AU - Paulus D.
AU - Grzegorzek M.
PY - 2013
SP - 167
EP - 175
DO - 10.5220/0004255601670175