Abstract
Selection is a fundamental task in volume visualization as it is often the first step for manipulation and analysis tasks. The presented work describes and investigates a novel 3-Dimensional (3D) selection technique for dense clouds of points. This technique solves issues with current selection techniques employed in such applications by allowing users to select similar regions of datasets without requiring prior knowledge about the structures within the data, thus bypassing occlusion and high density. We designed a prototype and experimented on large dense volumetric datasets. The preliminary results of our performance evaluation and the user-simulated test show encouraging results and indicate in which environments this technique could have high potential.
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References
Ward, M., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. A.K. Peters Ltd, Natick (2010)
Bowman, D.A., Kruijff, E., LaViola, J.J., Poupyrev, I.: 3D User Interfaces: Theory and Practice. Addison Wesley Longman Publishing Co. Inc, Redwood City (2004)
Bowman, D.A., Kruijff, E., LaViola, J.J., Poupyrev, I.: An introduction to 3-D user interface design. In: Presence: Teleoperations and Virtual Environments, vol. 10, pp. 96–108 (2001)
Grossman, T., Balakrishnan, R.: The design and evaluation of selection techniques for 3D volumetric displays. In: Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology UIST 2006, pp. 3–12. ACM, New York (2006)
Poupyrev, I., Weghorst, S., Billinghurst, M., Ichikawa, T.: Egocentric object manipulation in virtual environments: Empirical evaluation of interaction techniques (1998)
Yu, L., Efstathiou, K., Isenberg, P., Isenberg, T.: Efficient structure-aware selection techniques for 3D point cloud visualizations with 2DOF input. IEEE Trans. Visual. Comput. Graph. 18, 2245–2254 (2012)
Ulinski, A., Zanbaka, C., Wartell, Z., Goolkasian, P., Hodges, L.: Two handed selection techniques for volumetric data. In: 2007 IEEE Symposium on 3D User Interfaces, 3DUI 2007 (2007)
Jang, J., Rossignac, J.R.: Multiple object selection in pattern hierarchies (2007)
Kamat, V.R.: Enabling 3D visualization of simulated construction operations. Ph.D. thesis (2000)
Huang, R., Ma, K.L.: RGVis: region growing based techniques for volume visualization. In: 2003 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications, pp. 355–363 (2003)
Zhou, L., Hansen, C.: Transfer function design based on user selected samples for intuitive multivariate volume exploration. In: 2013 IEEE Pacific Visualization Symposium (PacificVis), pp. 73–80 (2013)
Licklider, J.C.R.: Man-computer symbiosis. In: Transactions on Human Factors in Electronics, vol. 1, pp. 4–11 (1960)
Steinbach, M., Ertz, L., Kumar, V.: The challenges of clustering high dimensional data. In: Wille, L. (ed.) New Directions in Statistical Physics, pp. 273–309. Springer, Heidelberg (2004)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc, Upper Saddle River (1988)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp.226–231. AAAI Press (1996)
Gaonkar, N., Sawant, K.: AutoEpsDBSCAN: Dbscan with EPS automatic for large dataset. IJACTE 2, 11–16 (2013)
Steinley, D.: Properties of the hubert-arable adjusted rand index. In: Psychological methods, vol. 9, p. 386 (2004)
Wertheimer, M.: Untersuchungen zur lehre von der gestalt. II. Psychologische Forsch. 4, 301–350 (1923)
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Sakou, L.B., Wilches, D., Banic, A. (2015). Region Growing Selection Technique for Dense Volume Visualization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_70
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DOI: https://doi.org/10.1007/978-3-319-27863-6_70
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