Abstract
Streamline querying is one of the important research directions in flow visualization, which can be widely used in streamline clustering, feature detection, etc. And the querying accuracy is the key to this field. So this paper proposes a new querying method with higher accuracy for streamlines in 3D flow visualization than the state-of-the-art methods. We define the finite substructures which are constructed by four neighboring equidistant sampling points from the streamlines. Firstly, we propose a new uniform segmentation method to transform the streamline to substructure sets. Then, by regarding the substructure as the ‘character’ and the substructure sets as ‘string,’ we evaluate the similarity of each streamline with the edit distance of strings. Finally, we specially design an algorithm for streamline querying by control functions so as to demonstrate the effectiveness of our new method.
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Brun A, Knutsson H, Park HJ, Shenton ME, Westin CF (2004) Clustering fiber traces using normalized cuts. In: Medical image computing and computer-assisted intervention, pp 368–375
Brun A, Park HJ, Knutsson H, Westin CF (2003) In: International conference on computer aided systems theory, Berlin, pp 518–529
Bujack R, Hotz I, Scheuermann G, Hitzer E (2014) Moment invariants for 2d flow fields using normalization. In: Visualization symposium, Washington, pp 41–48
Bujack R, Kasten J, Hotz I, Scheuermann G, Hitzer E (2015) Moment invariants for 3d flow fields via normalization. In: 2015 IEEE Pacific visualization symposium (PacificVis), pp 9–16
Camarri S, Salvetti MV, Buffoni M, Iollo A (2005) Simulation of the three-dimensional flow around a square cylinder between parallel walls at moderate Reynolds numbers. In: XVII Congresso di Meccanica Teorica ed Applicata
Chen CK, Yan S, Yu H, Max N, Ma KL (2011) An illustrative visualization framework for 3d vector fields. Comput Gr Forum 30(7):1941–1951
Chen W, Zhang S, Correia S, Ebert DS (2008) Abstractive representation and exploration of hierarchically clustered diffusion tensor fiber tracts. In: Joint eurographics/IEEE-VGTC conference on visualization, Chichester, pp 1071–1078
Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4(4):629–642
Karch GK, Sadlo F, Weiskopf D, Ertl T (2016) Visualization of 2d unsteady flow using streamline-based concepts in space–time. J Vis 19(1):115–128
Li Y, Wang C, Shene CK (2013) Streamline similarity analysis using bag-of-features, 9017:90170N
Lu K, Chaudhuri A, Lee TY, Shen HW, Wong PC (2013) Exploring vector fields with distribution-based streamline analysis. In: Pacific visualization symposium, pp 257–264
McLoughlin T, Laramee RS, Peikert R, Post FH, Chen M (2010) Over two decades of integration-based, geometric flow visualization. Comput Gr Forum 29(6):1807–1829
McLoughlin T, Jones MW, Laramee RS, Malki R, Masters I, Hansen CD (2013a) Similarity measures for enhancing interactive streamline seeding. IEEE Trans Vis Comput Gr 19(8):1342–1353
McLoughlin T, Jones MW, Laramee RS, Malki R, Masters I, Hansen CD (2013b) Similarity measures for enhancing interactive streamline seeding. IEEE Trans Vis Comput Gr 19(8):1342–1353
Moberts B, Vilanova A, van Wijk JJ (2005) Evaluation of fiber clustering methods for diffusion tensor imaging. In: VIS 05. IEEE visualization, pp 65–72
Park J, Lee KO, Han JH (2016) Interactive visualization of magnetic field for virtual science experiments. J Vis 19(1):129–139
Rossl C, Theisel H (2012) Streamline embedding for 3d vector field exploration. IEEE Trans Vis Comput Gr 18(3):407–420
Salzbrunn T, Scheuermann G (2006) Streamline predicates. IEEE Trans Vis Comput Gr 12(6):1601–1612
Salzbrunn T, Garth C, Scheuermann G, Meyer J (2008) Pathline predicates and unsteady flow structures. Vis Comput 24(12):1039–1051
Schlemmer M, Heringer M, Morr F, Hotz I, Hering-Bertram M, Garth C, Kollmann W, Hamann B, Hagen H (2007) Moment invariants for the analysis of 2d flow fields. IEEE Trans Vis Comput Gr 13(6):1743–1750
Schultz T (2011) Feature extraction for DW-MRI visualization: the state of the art and beyond. Scientific visualization: interactions, features, metaphors, dagstuhl follow-ups, vol 2. Dagstuhl, Wadern, pp 322–345
Shimony JS, Snyder AZ, Lori N, Contum TE (2003) Automated fuzzy clustering of neuronal pathways in diffusion tensor tracking. In: International society for magnetic resonance in medicine, p 453–456
Shi K, Theisel H, Hauser H, Weinkauf T, Matkovic K, Hege HC, Seidel HP (2009) Path line attributes - an information visualization approach to analyzing the dynamic behavior of 3D time-dependent flow fields, Berlin, pp 75–88
Song H, Liu S (2016) Dynamic fluid visualization based on multi-level density. In: Computer animation and social agents, CASA ’16, pp 193–196
Tao J, Wang C, Shene CK, Shaw RA (2016) A vocabulary approach to partial streamline matching and exploratory flow visualization. IEEE Trans Vis Comput Gr 22(5):1503–1516
Tao J, Wang C, Shene CK (2014) Flowstring: partial streamline matching using shape invariant similarity measure for exploratory flow visualization. In: IEEE Pacific visualization symposium, pp 9–16
Ukkonen E (1995) On-line construction of suffix trees. Algorithmica 14(3):249–260
Wang Z, Esturo JM, Seidel HP, Weinkauf T (2014) Pattern search in flows based on similarity of stream line segments. In: Vision, modeling and visualization. The Eurographics Association
Wei J, Wang C, Yu H, Ma KL (2010) A sketch-based interface for classifying and visualizing vector fields. In: Pacific visualization symposium, pp 129–136
Ye X, Kao D, Pang A (2005) Strategy for seeding 3d streamlines. In: VIS 05. IEEE visualization, pp 471–478
Zhang S, Correia S, Laidlaw DH (2008) Identifying white-matter fiber bundles in dti data using an automated proximity-based fiber-clustering method. IEEE Trans Vis Comput Gr 14(5):1044–1053
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This work was partly supported by the Natural Science Foundation of China under grant nos. 61170118 and 61672375, the National Key R&D Program of China under no. 2018YFC1407405.
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Liu, S., Song, H. Streamline querying based on finite substructures. J Vis 22, 571–585 (2019). https://doi.org/10.1007/s12650-019-00552-x
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DOI: https://doi.org/10.1007/s12650-019-00552-x