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Issue No.07 - July (2013 vol.19)

pp: 1122-1132

V. Matvienko , IVDA group, Saarland Univ. Cluster of Excellence MMCI, Saarbrucken, Germany

Jens Kruger , IVDA, DFKI, Saarbrucken, Germany

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.170

ABSTRACT

In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric's applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.

INDEX TERMS

Measurement, Visualization, Vectors, Equations, Data visualization, Noise, Humans,LIC, Visualization evaluation, texture-based visualization

CITATION

V. Matvienko, Jens Kruger, "A Metric for the Evaluation of Dense Vector Field Visualizations",

*IEEE Transactions on Visualization & Computer Graphics*, vol.19, no. 7, pp. 1122-1132, July 2013, doi:10.1109/TVCG.2012.170REFERENCES

- [1] B. Cabral and L.C. Leedom, “Imaging Vector Fields Using Line Integral Convolution,”
SIGGRAPH '93: Proc. 20th Ann. Conf. Computer Graphics and Interactive Techniques, pp. 263-270, 1993.- [2] F.H. Post, B. Vrolijk, H. Hauser, R.S. Laramee, and H. Doleisch, “The State of the Art in Flow Visualisation: Feature Extraction and Tracking,”
Computer Graphics Forum, vol. 22, no. 4, pp. 775-792, 2003.- [3] T. Salzbrunn, H. Jänicke, T. Wischgoll, and G. Scheuermann, “The State of the Art in Flow Visualization: Partition-Based Techniques,”
Proc. Simulation and Visualization (SimVis), pp. 75-92, 2008.- [4] R.S. Laramee, H. Hauser, H. Doleisch, B. Vrolijk, F.H. Post, and D. Weiskopf, “The State of the Art in Flow Visualization: Dense and Texture-Based Techniques,”
Computer Graphics Forum, vol. 23, pp. 203-221, 2004.- [5] J.J. van Wijk, “Spot Noise Texture Synthesis for Data Visualization,”
SIGGRAPH Computational Graphics, vol. 25, pp. 309-318, July 1991.- [6] D. Weiskopf,
GPU-Based Interactive Visualization Techniques, Mathematics and Visualization. Springer-Verlag, 2006.- [7] A. Okada and D. Lane, “Enhanced Line Integral Convolution with Flow Feature Detection,”
Proc. SPIE Visual Data Exploration and Analysis IV, vol. 3017, pp. 206-217, 1997.- [8] D. Weiskopf, “Iterative Twofold Line Integral Convolution for Texture-Based Vector Field Visualization,”
Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, series Mathematics and Visualization, pp. 191-211, Springer, 2009.- [9] M. Hlawatsch, F. Sadlo, and D. Weiskopf, “Hierarchical Line Integration,”
IEEE Trans. Visualization and Computer Graphics, vol. 17, no. 8, pp. 1148-1163, Aug. 2011.- [10] R.L. Cook and T. DeRose, “Wavelet Noise,”
Proc. ACM SIGGRAPH Papers, pp. 803-811, 2005.- [11] D.H. Laidlaw, J.S. Davidson, T.S. Miller, M. da Silva, R.M. Kirby, W.H. Warren, and M. Tarr, “Quantitative Comparative Evaluation of 2D Vector Field Visualization Methods,”
Proc. Conf. Visualization '01, pp. 143-150, 2001.- [12] D.H. Laidlaw, R.M. Kirby, C.D. Jackson, J.S. Davidson, T.S. Miller, M. da Silva, W.H. Warren, and M.J. Tarr, “Comparing 2D Vector Field Visualization Methods: A User Study,”
IEEE Trans. Visualization and Computer Graphics, vol. 11, no. 1, pp. 59-70, Jan. 2005.- [13] D. Pineo and C. Ware, “Neural Modeling of Flow Rendering Effectiveness,”
Proc. Fifth Symp. Applied Perception in Graphics and Visualization (APGV '08), pp. 171-178, 2008.- [14] A. Forsberg, J. Chen, and D. Laidlaw, “Comparing 3D Vector Field Visualization Methods: A User Study,”
IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, pp. 1219-1226, Nov./Dec. 2009.- [15] D. Stalling, “Fast Texture-Based Algorithms for Vector Field Visualization,” PhD dissertation, Zuse Inst. of Berlin, 1998.
- [16] D. Stalling and H.-C. Hege, “Fast and Resolution Independent Line Integral Convolution,”
SIGGRAPH '95: Proc. 22nd Ann. Conf. Computer Graphics and Interactive Techniques, pp. 249-256, 1995.- [17] Q. Cui, M. Ward, E. Rundensteiner, and J. Yang, “Measuring Data Abstraction Quality in Multiresolution Visualizations,”
IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 5, pp. 709-716, Sept./Oct. 2006.- [18] C. Chen, “Measuring the Quality of Network Visualization,”
Proc. ACM/IEEE-CS Fifth Joint Conf. Digital Libraries (JCDL '05), p. 405, 2005.- [19] J.J. van Wijk, “The Value of Visualization,”
Proc. IEEE Visualization (VIS '05), pp. 79-86, 2005.- [20] D. Filonik and D. Baur, “Measuring Aesthetics for Information Visualization,”
Proc. Int'l Conf. Information Visualisation, pp. 579-584, 2009.- [21] H. Jänicke and M. Chen, “A Salience-Based Quality Metric for Visualization,”
Computer Graphics Forum, vol. 29, no. 3, pp. 1183-1192, 2010.- [22] H. Jänicke, T. Weidner, D. Chung, R.S. Laramee, P. Townsend, and M. Chen, “Visual Reconstructability as a Quality Metric for Flow Visualization,”
Computer Graphics Forum, vol. 30, no. 3, pp. 781-790, 2011.- [23] Wikipedia, “Occam's Razor,” http://en.wikipedia.org/wiki Occam's_razor , 2011.
- [24] R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,”
Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 1137-1143, 1995.- [25] M.-H. Kiu and D.C. Banks, “Multi-Frequency Noise for LIC,”
Proc. Seventh Conf. Visualization '96, pp. 121-126, 1996.- [26] A. Sanna, C. Zunino, B. Montrucchio, and P. Montuschi, “Adding a Scalar Value to Texture-Based Vector Field Representations by Local Contrast Analysis,”
Proc. Symp. Data Visualisation (VISSYM '02), pp. 35-41, 2002.- [27] D. Weiskopf, G. Erlebacher, and T. Ertl, “A Texture-Based Framework for Spacetime-Coherent Visualization of Time-Dependent Vector Fields,”
Proc. IEEE 14th Visualization (VIS '03), pp. 15-, 2003.- [28] O. Frederich, E. Wassen, and F. Thiele, “Prediction of the Flow Around a Short Wall-Mounted Cylinder Using LES and DES,”
J. Numerical Analysis, Industrial and Applied Math., vol. 3, pp. 231-247, 2008. |