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Issue No.12 - Dec. (2011 vol.17)

pp: 1959-1968

Ziyi Zheng , Stony Brook University

Nafees Ahmed , Stony Brook University

Klaus Mueller , Stony Brook University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.218

ABSTRACT

The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.

INDEX TERMS

Direct volume rendering, k-means, entropy, view suggestion, set-cover problem, ant colony optimization.

CITATION

Ziyi Zheng, Nafees Ahmed, Klaus Mueller, "iView: A Feature Clustering Framework for Suggesting Informative Views in Volume Visualization",

*IEEE Transactions on Visualization & Computer Graphics*, vol.17, no. 12, pp. 1959-1968, Dec. 2011, doi:10.1109/TVCG.2011.218REFERENCES

- [1] S. Abbasi and F. Mokhtarian, “Automatic view selection in multi-view object recognition,”
In Proc. of International Conference on Pattern Recognition, volume 1, pages 13-16, 2000.- [2] A. Amirkhanov, C. Heinzl, M. Reiter, and M. E. Gröller, “Visual optimality and stability analysis of 3DCT scan positions,”
IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1477-1487, 2010.- [3] J. E. Beasley, “OR-Library: distributing test problems by electronic mail,”
Journal Operational Research Society, 41 (11): 1069-1072, 1990.- [4] P. S. Blaer and P. K. Allen, “View planning and automated data acquisition for 3-D modeling of complex sites”,
Journal of Field Robotics, 26 (11): 865-891, 2009.- [5] U. Bordoloi and H.-W. Shen, “View selection for volume rendering,”
In Proceedings of the IEEE Visualization, pages 487-494, 2005.- [6] M.-Y. Y, H. Qu, K.-K. K, W.-H. H, and Y. Wu, “Relation-aware volume exploration pipeline”.
IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1683-1690, 2008.- [7] M.-Y. Y, Y. Wu, W.-H. H, W. Chen, and H. Qu, “Perception-based transparency optimization for direct volume rendering,”
IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1283-1290, 2009.- [8] C. Correa and K.-L. L, “Size-based transfer functions: a new volume exploration technique,”
IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1380-1387, 2008.- [9] C. Correa and K.-L. L, “The occlusion spectrum for volume classification and visualization,”
IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1465-1472, 2009.- [10] C. Correa and K.-L. L, “Visibility histograms and visibility-driven transfer functions,”
IEEE Transactions on Visualization and Computer Graphics, 17 (2): 192-204, 2011.- [11] D. DeCarlo, A. Finkelstein, S. Rusinkiewicz, and A. Santella, “Suggestive contours for conveying shape,”
ACM Transactions on Graphics, 22 (3): 848-855, 2003.- [12] T. Fogal and J. Krüger, “Tuvok - an architecture for large scale volume rendering,”
In Proceedings of the 15th International Workshop on Vision, Modeling, and Visualization, 2010.- [13] W. Fang, K.-K. K, M. Lu, X. Xiao, C.-K. Lam, and P. Y. Yang, B. He, Q. Luo, P. V. Sander, and K. Yang, “Parallel data mining on graphics processors,”
Technical Report, HKUST-CS08-07, 2008.- [14] S. Fleishman, D. Cohen-Or, and D. Lischinski, “Automatic camera placement for image-based modeling,”
Computer Graphics Forum, 19 (2): 101-110, 2000.- [15] http:/www.imagevis3d.org, ImageVis3D: A real-time volume rendering tool for large data. Scientific Computing and Imaging Institute (SCI).
- [16] C. Harris and M. Stephens, “A combined corner and edge detector,”
Proc. 4th Alvey Vision Conf. pp. 147–151, 1988.- [17] R. Karp, “Reducibility among combinatorial problems,”
Complexity of Computer Computations. pp. 85-103, 1972.- [18] A. E. Kaufman, S. Lakare, K. Kreeger, and I. Bitter, “Virtual colonoscopy,”
Communication of the ACM, 48 (2): 37-41, 2005.- [19] D. J. Ketchen and C. L. Shook, “The application of cluster analysis in strategic management research: an analysis and critique,”
Strategic Management Journal, 17 (6): 441-458, 1996.- [20] G. Kindlmann, R. Whitaker, T. Tasdizen, and T. Möller, “Curvature-based transfer functions for direct volume rendering: methods and applications,”
In Proceedings of the IEEE Visualization, pages 513-520, 2003.- [21] J. Kniss, G. Kindlmann, and C. Hansen, “Multi-dimensional transfer functions for interactive volume rendering,”
IEEE Transactions on Visualization and Computer Graphics, 8 (3): 270-285, 2002.- [22] P. Kohlmann, S. Bruckner, A. Kanitsar, and M. E. Gröller, “LiveSync: deformed viewing spheres for knowledge-based navigation,”
IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1544-1551, 2007.- [23] J. Krüger, J. Schneider, and R. Westermann, “ClearView: An interactive context preserving hotspot visualization technique,”
IEEE Transactions on Visualization and Computer Graphics, 12 (5): 941-948, 2006.- [24] D. Lowe, “Distinctive image features from scale-invariant keypoints,”
Intern. J. Computer Vision, 60 (2): 91-110, 2004.- [25] R. Maciejewski, I. Woo, W. Chen, and D. Ebert, “Structuring feature space: a non-parametric method for volumetric transfer function generation,”
IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1473-1480, 2009.- [26] W.-H. H, Y. Wu, M.-Y. Y, and H. Qu, “Visibility-aware direct volume rendering,”
Journal of Computer Science and Technology, 26 (2): 217-228, 2011.- [27] J. Nam, M. Mauer, and K. Mueller, “High dimensional feature descriptors to characterize volumetric data,”
Knowledge-Assisted Visualization Workshop, 2008.- [28] D. Pelleg and A. Moore, “X-means: extending k-means with efficient estimation of the number of clusters,”
In Proc. of the 17th International Conference on Machine Learning, pages 727-734, 2000.- [29] R. Pito, “A solution to the next best view problem for automated surface acquisition,”
IEEE Trans. of Pattern Analysis and Maching Intelligence, 21 (10): 1016-1030, 1999.- [30] M. Rahoual, R. Hadji, and V. Bachelet, “Parallel ant system for the set covering problem,”
Lecture Notes in Computer Science, volume 2463, pages 249-297, 2002.- [31] Z. Ren, Z. Feng, L. Ke, and Z. Zhang, “New ideas for applying ant colony optimization to the set covering problem,”
Computers and Industrial Engineering, 58 (4): 774-784, 2010.- [32] W. Scott, G. Roth, and J. Rivest, “View planning for automated three-dimensional object reconstruction and inspection,”
ACM Computing Surveys, 35 (1): 64-96, 2003.- [33] P. Sereda, A. Vilanova, and F. A. Gerritsen, “Automating transfer function design for volume rendering using hierarchical clustering of material boundaries,”
In Proc. of EuroVis 2006. pages 243-250, 2006.- [34] N. Sudarsanam, K. Singh, and C. Grimm, “Non-linear perspective widgets for creating multiple-view images,”
Symposium on Non-photorealistic Animation and Rendering, pages 69-79, 2008.- [35] S. Takahashi, I. Fujishiro, Y. Takeshima, and T. Nishita, “A feature-driven approach to locating optimal viewpoints for volume visualization,”
In Proceedings of the IEEE Visualization, pp. 495-502, 2005.- [36] P.-P. P, M. Feixas, M. Sbert, and W. Heidrich, “Viewpoint selection using view entropy,”
In Proc. of Vision Modeling and Visualization Conference, pages 273-280, 2001.- [37] P.-P. P, M. Feixas, M. Sbert, and W. Heidrich, “Automatic view selection using viewpoint entropy and its application to image-based modeling,”
Computer Graphics Forum, 22 (4): 689-700, 2003.- [38] I. Viola, A. Kanitsar, and M. E. Gröller, “Importance-driven feature enhancement in volume visualization”,
IEEE Transactions on Visualization and Computer Graphics, 11 (4): 408-418, 2005.- [39] L. Wang, Y. Zhao, K. Mueller, and A. E. Kaufman, “The magic volume lens: an interactive focus+context technique for volume rendering,”
In Proceedings of the IEEE Visualization, pages 367-374, 2005.- [40] S. Wenhardt, B. Deutsch, J. Hornegger, H. Niemann, and J. Denzler, “An information theoretic approach for next best view planning in 3-D reconstruction,”
In Proc. of International Conference on Pattern Recognition, pp.103-106, 2006.- [41] Y. Wu, K.-K. K, H. Qu, X. Yuan, and S.-C. C, “Interactive visual optimization and analysis for RFID benchmarking,”
IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1335-1342, 2009.- [42] R. Zhang, P.-S. S, J. E. Cryer, and M. Shah, “Shape from shading: a survey,”
IEEE Trans. of Pattern Analysis and Maching Intelligence, 21 (8): 690-706, 1999. |