Skip to main content
SpringerLink
Log in

Salient Point Detection in Protrusion Parts of 3D Object Robust to Isometric Variations

  • 3DR Express
  • Published:
3D Research

Abstract

In this paper, a novel method is proposed to detect 3D object salient points robust to isometric variations and stable against scaling and noise. Salient points can be used as the representative points from object protrusion parts in order to improve the object matching and retrieval algorithms. The proposed algorithm is started by determining the first salient point of the model based on the average geodesic distance of several random points. Then, according to the previous salient point, a new point is added to this set of points in each iteration. By adding every salient point, decision function is updated. Hence, a condition is created for selecting the next point in which the iterative point is not extracted from the same protrusion part so that drawing out of a representative point from every protrusion part is guaranteed. This method is stable against model variations with isometric transformations, scaling, and noise with different levels of strength due to using a feature robust to isometric variations and considering the relation between the salient points. In addition, the number of points used in averaging process is decreased in this method, which leads to lower computational complexity in comparison with the other salient point detection algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Liu, J., & Wang, S. (2015). Salient region detection via simple local and global contrast representation. Neurocomputing, 147, 435–443.

    Article  Google Scholar 

  2. Dutagaci, H., Cheung, C. P., & Godil, A. (2012). Evaluation of 3D interest point detection techniques via human-generated ground truth. The Visual Computer, 28, 901–917.

    Article  Google Scholar 

  3. Pedrosa, G. V., Traina, A. J., & Barcelos, C. A. (2017). Retrieving 2D shapes by similarity based on bag of salience points. Multimedia Tools and Applications, 76, 20957–20971.

    Article  Google Scholar 

  4. Pedrosa, G. V., & Barcelos, C. A. (2010). Anisotropic diffusion for effective shape corner point detection. Pattern Recognition Letters, 31, 1658–1664.

    Article  Google Scholar 

  5. Jin, C., & Ke, S.-W. (2017). Content-based image retrieval based on shape similarity calculation. 3D Research, 8, 23.

    Article  Google Scholar 

  6. Yang, Y., Hu, X., Wu, N., Wang, P., Xu, D., & Rong, S. (2017). A depth map generation algorithm based on saliency detection for 2D to 3D conversion. 3D Research, 8, 29.

    Article  Google Scholar 

  7. Zhao, Y., Liu, Y., Wang, Y., Wei, B., Yang, J., Zhao, Y., et al. (2016). Region-based saliency estimation for 3D shape analysis and understanding. Neurocomputing, 197, 1–13.

    Article  Google Scholar 

  8. Hajizadeh, M., & Ebrahimnezhad, H. (2016). Predictive compression of animated 3D models by optimized weighted blending of key-frames. Computer Animation and Virtual Worlds, 27, 556–576.

    Article  Google Scholar 

  9. Agathos, A., Pratikakis, I., Perantonis, S., & Sapidis, N. S. (2010). Protrusion-oriented 3D mesh segmentation. The Visual Computer, 26, 63–81.

    Article  Google Scholar 

  10. Atmosukarto, I., Wilamowska, K., Heike, C., & Shapiro, L. G. (2010). 3D object classification using salient point patterns with application to craniofacial research. Pattern Recognition, 43, 1502–1517.

    Article  Google Scholar 

  11. Medimegh, N., Belaid, S., Atri, M, & Werghi, N. (2016). Statistical robust watermarking for 3D mesh models based on salient points. In Advanced Technologies for Signal and Image Processing (ATSIP), 2016 2nd International Conference on, pp. 52–56.

  12. Mohamed, H. H., & Belaid, S. (2015). Algorithm boss (bag-of-salient local spectrums) for non-rigid and partial 3D object retrieval. Neurocomputing, 168, 790–798.

    Article  Google Scholar 

  13. Liu, Y., Zhang, X., Li, Z., & Li, H. (2013). Extended cone-curvature based salient points detection and 3D model retrieval. Multimedia Tools and Applications, 64, 671–693.

    Article  Google Scholar 

  14. Quan, L., & Tang, K. (2015). Polynomial local shape descriptor on interest points for 3D part-in-whole matching. Computer-Aided Design, 59, 119–139.

    Article  Google Scholar 

  15. Bronstein, A. M., Bronstein, M. M., Bustos, B., Castellani, U., Crisani, M., Falcidieno, B., Guibas, L. J., Kokkinos, I., Murino, V, & Ovsjanikov, M. (2010). SHREC 2010: Robust feature detection and description benchmark.In Proceedings of 3DOR, vol. 2, p. 6.

  16. Boyer, E., Bronstein, A. M., Bronstein, M. M., Bustos, B., Darom, T., Horaud, R., Hotz, I., Keller, Y., Keustermans, J, & Kovnatsky, A. (2011). SHREC 2011: Robust feature detection and description benchmark. arXiv preprint arXiv:1102.4258.

  17. Ebrahimnezhad, H., & Ghassemian, H. (2008). Robust motion from space curves and 3D reconstruction from multiviews using perpendicular double stereo rigs. Image and Vision Computing, 26, 1397–1420.

    Article  Google Scholar 

  18. Tombari, F., Salti, S., & Di Stefano, L. (2013). Performance evaluation of 3D keypoint detectors. International Journal of Computer Vision, 102, 198–220.

    Article  Google Scholar 

  19. Reuter, M., Wolter, F.-E., & Peinecke, N. (2006). Laplace–Beltrami spectra as ‘Shape-DNA’of surfaces and solids. Computer-Aided Design, 38, 342–366.

    Article  Google Scholar 

  20. Prakhya, S. M., Liu, B., Lin, W., Jakhetiya, V., & Guntuku, S. C. (2016). B-SHOT: A binary 3D feature descriptor for fast Keypoint matching on 3D point clouds. Autonomous Robots, pp. 1–20.

  21. Chen, H., & Bhanu, B. (2007). 3D free-form object recognition in range images using local surface patches. Pattern Recognition Letters, 28, 1252–1262.

    Article  Google Scholar 

  22. Zhong, Y. (2009) Intrinsic shape signatures: A shape descriptor for 3d object recognition. In: Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pp. 689–696.

  23. Mian, A., Bennamoun, M., & Owens, R. (2010). On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes. International Journal of Computer Vision, 89, 348–361.

    Article  Google Scholar 

  24. Agathos, A., Pratikakis, I., Perantonis, S., Sapidis, N., & Azariadis, P. (2007). 3D mesh segmentation methodologies for CAD applications. Computer-Aided Design and Applications, 4, 827–841.

    Article  Google Scholar 

  25. Katz, S., Leifman, G., & Tal, A. (2005). Mesh segmentation using feature point and core extraction. The Visual Computer, 21, 649–658.

    Article  Google Scholar 

  26. Lin, H.-Y. S., Liao, H.-Y. M., & Lin, J.-C. (2007). Visual salience-guided mesh decomposition. IEEE Transactions on Multimedia, 9, 46–57.

    Article  Google Scholar 

  27. Mirloo, M. & Ebrahimnezhad, H. (2017) Non-rigid 3D object retrieval using directional graph representation of wave kernel signature. Multimedia Tools and Applications, pp. 1–25.

  28. Del Alamo, C. J. L., Calla, L. A. R., & Pérez, L. J. F. (2015). Efficient approach for interest points detection in non-rigid shapes. In Computing Conference (CLEI), 2015 Latin American, pp. 1–8.

  29. Walter, N., Aubreton, O., & Laligant, O. (2008) Salient point characterization for low resolution meshes. In Image Processing, 2008, ICIP 2008. 15th IEEE International Conference on, pp. 1512–1515.

  30. Sipiran, I., & Bustos, B. (2011). Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. The Visual Computer, 27, 963–976.

    Article  Google Scholar 

  31. Castellani, U., Cristani, M., Fantoni, S., & Murino, V. (2008) Sparse points matching by combining 3D mesh saliency with statistical descriptors. In Computer Graphics Forum, pp. 643–652.

  32. Sun, J., Ovsjanikov, M., & Guibas, L. (2009) A concise and provably informative multi‐scale signature based on heat diffusion. In Computer graphics forum, pp. 1383–1392.

  33. Chazal, F., Guibas, L. J., Oudot, S. Y., & Skraba, P. (2009). Analysis of scalar fields over point cloud data. In Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1021–1030.

  34. Peyré, G. (2011). The numerical tours of signal processing. Computing in Science & Engineering, 13, 94–97.

    Article  Google Scholar 

  35. Surazhsky, V., Surazhsky, T., Kirsanov, D., Gortler, S. J., & Hoppe, H. (2005) Fast exact and approximate geodesics on meshes. In ACM Transactions on Graphics (TOG), pp. 553–560.

  36. Pears, N., Liu, Y., & Bunting, P. (2012). 3D imaging, analysis and applications vol. 3: Springer.

Download references

Author information

Authors and Affiliations

  1. Computer Vision Research Lab, Electrical Engineering Faculty, Sahand University of Technology, Tabriz, Iran

    Mahsa Mirloo & Hosein Ebrahimnezhad

Authors
  1. Mahsa Mirloo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hosein Ebrahimnezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hosein Ebrahimnezhad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirloo, M., Ebrahimnezhad, H. Salient Point Detection in Protrusion Parts of 3D Object Robust to Isometric Variations. 3D Res 9, 2 (2018). https://doi.org/10.1007/s13319-018-0155-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13319-018-0155-1

Keywords

Search

Navigation