This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Texture-based feature tracking for effective time-varying data visualization
November/December 2007 (vol. 13 no. 6)
pp. 1472-1479
Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.

[1] S. Baker and I. Matthews, Equivalence and efficiency of image alignment algorithms. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1090–1097, 2001.
[2] S. Belongie, C. Carson, H. Greenspan, and J. Malik, Color- and texture-based image segmentation using EM and its application to content-based image retrieval. In Proceedings of the Sixth International Conference on Computer Vision, pages 675–682, 1998.
[3] C. I. Christodoulou, C. S. Pattichis, M. Pantziaris, and A. Nicolaides, Texture-based classification of atherosclerotic carotid plaques. volume 22, pages 902–912, July 2003.
[4] X. Guan, G. Pan, and Z. Wu, Automatic categorization of traditional chinese painting images with statistical gabor feature and color feature. In Lectures in Computer Science, pages 743–750. Springer Berlin/Heidelberg, 2005.
[5] R. Haralick, K. Shanmugam, and I. Dinstein, Textural features for image classification. In IEEE Transactions on Systems, Man, and Cybernetics, volume 3 pages 610–621, 1973.
[6] H. Hauser, L. Mroz, G. I. Bischi, and M. E. Gröller, Two-level volume rendering. volume 7, pages 242–252, 2001.
[7] G. Ji, H.-W. Shen, and R. Wenger, Volume tracking using higher dimensional isosurfacing. In IEEE Visualization '03, pages 209–216, 2003.
[8] L. Matthews, T. Ishikawa, and S. Baker, The template update problem. In IEEE Pattern Analysis and Machine Intelligence, volume 26, pages 810–815, June 2004.
[9] H. Peng, F. Long, and C. Ding, Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 27, pages 1226–1238, 2005.
[10] F. Reinders, F. H. Post, and H. J. W. Spoelder, Attribute-based feature tracking. In Data Visualization '99, pages 63–72. Springer-Verlag Wien, 1999.
[11] P. Rheingans and D. Ebert, Volume illustration: Non-photorealistic rendering of volume models. In IEEE Transactions on Visualization and Computer Graphics, volume 7 (3), pages 253–264, 2001.
[12] R. Samtaney, D. Silver, N. Zabusky, and J. Cao, Visualizing features and tracking their evolution. volume 27, pages 20–27. IEEE Computer Society Press, 1994.
[13] D. Silver and X. Wang, Volume tracking. In R. Yagel and G. M. Nielson, editors, IEEE Visualization '96, pages 157–164, 1996.
[14] D. Silver and X. Wang, Tracking scalar features in unstructured datasets. In IEEE Visualization '98, pages 79–86, 1998.
[15] X. Tang, Texture information in run-length matrices. In IEEE Transactions of Image Processing, volume 7, pages 1602–1609, 1998.
[16] F.-Y. Tzeng and K.-L. Ma, Intelligent feature extraction and tracking for large-scale 4d flow simulations. In IEEE Supercomputing Conference, pages 8–15, 2005.
[17] K. Wang, H. Qin, P. R. Fisher, and W. Zhao, Automatic registration of mammograms using texture-based anisotropic features. In Proceedings of 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pages 864–867, 2006.
[18] H. E. Willoughby, Atmosphere: Forecasting hurricane intensity and impacts. In Science, volume 315, page 1232, 2007.
[19] Y. Xu, M. Sonka, G. McLennan, J. Guo, and E. A. Hoffman, MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. volume 25, pages 464–475, 2006.

Index Terms:
Feature tracking, texture-based analysis, flow visualization, time-varying data, visualization
Citation:
Jesus Caban, Alark Joshi, Penny Rheingans, "Texture-based feature tracking for effective time-varying data visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1472-1479, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70599
Usage of this product signifies your acceptance of the Terms of Use.