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Computer Visualization of 3D objects using Feature Vector Based Methods

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IP Multimedia Communications
© 2011 by IJCA Journal
ISBN : 978-93-80864-99-3
Year of Publication: 2011
Authors:
Manju Mandot

Manju Mandot. Computer Visualization of 3D objects using Feature Vector Based Methods. Special issues on IP Multimedia Communications (1):107-110, October 2011. Full text available. BibTeX

@article{key:article,
	author = {Manju Mandot},
	title = {Computer Visualization of 3D objects using Feature Vector Based Methods},
	journal = {Special issues on IP Multimedia Communications},
	month = {October},
	year = {2011},
	number = {1},
	pages = {107-110},
	note = {Full text available}
}

Abstract

Recent development in the techniques for digitizing and visualizing 3D objects has led to an explosion the number of available of models on the internet and in domain specific databases. Various 3D objects have different styles and use different units so that desired properties of feature vector are invariance with respect to translation, rotation, reflection and scaling, robustness with respect to level-of-detail of a model and changeable dimension. A feature vector based methods are used for multimedia retrieval. In this paper we review recent methods for feature vector retrieval of 3D objects.

Reference

  1. Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., And Jacobs, D. 2003. A search engine for 3D models. ACM Trans. Graph. 22, 1, 83–105.
  2. L¨Offler, J. 2000. Content-based retrieval of 3D models in distributed web databases by visual shape information. In Proceedings of the International Conference on Information Visualisation (IV’00). IEEE Computer Society, Washington, DC, 82.
  3. A. LaurentinI, How far 3d shapes can be understood from 2d silhouettes," IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2):188-195, February 1995.
  4. Y. Gdalyahu and D. Weinshalll, Flexible Syntactic Matching of Curves and its Application to Automatic Hierarchical Classi_cation of Silhouettes," IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(12):1312-1328, 1999.
  5. Heczko, M., Keim, D. A., Saupe, D., and Vrani´C, D. 2002. Methods for similarity search on 3D databases. Datenbank-Spektrum 2, 2, 54–63. (In German)
  6. Faloutsos, C. 1996. Searching Multimedia Databases by Content. Kluwer Academic Publishers, Norwell, MA.
  7. Vrani´C, D. and Saupe, D. 2000. 3D model retrieval. In Proceedings of the Spring Conference on Computer Graphics and its Applications. Comenius University, 89–93.
  8. R. Osada, T. Funkhouser, B. Chazelle , and D. Dobkin, Matching 3D Models with Shape Distributions," in Proc. SMI 2001, Genova, Italy, May 2001, pp. 154-166.
  9. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin, Shape Distributions," ACM Transactions on Graphics, 21(4):807-832, October 2002.
  10. Paquet, E., Murching, A., Naveen, T., Tabatabai, A., and Rioux, M. 2000. Description of shape information for 2-D and 3-D objects. Signal Process. Image Comm. 16, 103–122.
  11. Ohbuchi, R., Otagiri, T., Ibato, M., and Takei, T. 2002. Shape-similarity search of three dimensional models using parameterized statistics. In Proceedings of the 10th Pacific Conference on Computer Graphics and Applications (PG’02). IEEE Computer Society, Washington, DC, 265–274.
  12. VRANI´C, D. AND SAUPE, D. 2001a. 3D model retrieval with spherical harmonics and moments. In Proceedings of the 23rd DAGM-Symposium on Pattern Recognition. Springer-Verlag, London, UK, 392–397.
  13. Elad, M., Tal, A., and AR, S. 2002. Content based retrieval of VRML objects: an iterative and interactive approach. In Proceedings of the 6th Eurographics Workshop on Multimedia 2001. Springer-Verlag New York, NY, 107–118.
  14. Zaharia, T. and PRˆEteux, F. 2001. Three dimensional shape-based retrieval within the MPEG-7 framework. In Proceedings of the SPIE Conference on Nonlinear Image Processing and Pattern Analysis XII. 133–145.
  15. HORN, B. 1984. Extended Gaussian image. Proceedings of the IEEE 72, 12, 1671–1686. IP, C. Y., Lapadat, D., Sieger, L., and Regli, W. C. 2002. Using shape distributions to compare solid models. In Proceedings of the 7th ACM Symposium on Solid Modeling and Applications (SMA’02). ACM Press, New York, NY, 273–280.
  16. Healy, D. M., Rockmore, D. N., Kostelec, P. J., Moore, S. S. B. 2003. FFTs for the 2-sphere -Improvements and variations. J. Fourier Analy. Appl. 9, 4, 341–385.