Sets of Globally Optimal Stream Surfaces for Flow Visualization

Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set...

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Veröffentlicht in:	Computer graphics forum Jg. 33; H. 3; S. 1 - 10
Hauptverfasser:	Schulze, M., Esturo, J. Martinez, Günther, T., Rössl, C., Seidel, H.-P., Weinkauf, T., Theisel, H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.06.2014
Schlagworte:	3-D graphics Analysis and systems Categories and Subject Descriptors (according to ACM CCS) Computer graphics Computer Science Computer simulation Datalogi Flow visualization Fluids I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems Image processing systems languages Mathematical analysis Nucleation Optimization SRA - E-Science (SeRC) SRA - E-vetenskap (SeRC) Streams Studies Three dimensional
ISSN:	0167-7055, 1467-8659, 1467-8659
Online-Zugang:	Volltext
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Abstract	Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study.
AbstractList	Stream surfaces are a well-studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time-consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study. Stream surfaces are a well-studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time-consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study. [PUBLICATION ABSTRACT] Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study.
Author	Theisel, H. Schulze, M. Rössl, C. Seidel, H.-P. Weinkauf, T. Esturo, J. Martinez Günther, T.
Author_xml	– sequence: 1 givenname: M. surname: Schulze fullname: Schulze, M. organization: University of Magdeburg – sequence: 2 givenname: J. Martinez surname: Esturo fullname: Esturo, J. Martinez organization: MPI for Informatics – sequence: 3 givenname: T. surname: Günther fullname: Günther, T. organization: University of Magdeburg – sequence: 4 givenname: C. surname: Rössl fullname: Rössl, C. organization: University of Magdeburg – sequence: 5 givenname: H.-P. surname: Seidel fullname: Seidel, H.-P. organization: MPI for Informatics – sequence: 6 givenname: T. surname: Weinkauf fullname: Weinkauf, T. organization: MPI for Informatics – sequence: 7 givenname: H. surname: Theisel fullname: Theisel, H. organization: University of Magdeburg
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Copyright	2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd.
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References	Fabbri R., Costa L. D. F., Torelli J.C., Bruno O.M.: 2D euclidean distance transform algorithms: A comparative survey. ACM Comput. Surv. 40, 1 (2008), 2:1-2:44. (Cited on page 4) McLoughlin T., Laramee R.S., Peikert R., Post F.H., Chen M.: Over two decades of integration-based, geometric flow visualization. CGF 29, 6 (2010), 1807-1829. (Cited on pages 2 and 9) Martinez Esturo J., Schulze M., Rössl C., Theisel H.: Global selection of stream surfaces. CGF (Proc. EG) 32, 2 (2013), 113-122. (Cited on pages 1, 2, 3, 4, 6, 7, 8, and 9) Edmunds M., Laramee R.S., Chen G., Max N., Zhang E., Ware C.: Surface-based flow visualization. C&G 36, 8 (2012), 974-990. (Cited on pages 1 and 2) Kirkpatrick S., Gelatt C.D., Vecchi M.P.: Optimization by simulated annealing. Science 220, 4598 (1983), 671-680. (Cited on page 3) Weinkauf T., Theisel H.: Streak lines as tangent curves of a derived vector field. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1225-1234. (Cited on page 9) Krüger J., Kipfer P., Kondratieva P., Westermann R.: A particle system for interactive visualization of 3D flows. IEEE TVCG 11, 6 (2005), 744-756. (Cited on page 2) Sabin M.: Analysis and Design of Univariate Subdivision Schemes. Springer, 2010. (Cited on page 6) Carnecky R., Fuchs R., Mehl S., Jang Y., Peikert R.: Smart transparency for illustrative visualization of complex flow surfaces. IEEE TVCG 19, 5 (2013), 838-851. (Cited on page 2) Günther T., Rössl C., Theisel H.: Opacity optimization for 3D line fields. ACM TOG (Proc. SIGGRAPH) 32, 4 (2013), 120-120. (Cited on pages 6 and 7) Laramee R.S., van Wijk J.J., Jobard B., Hauser H.: ISA and IBFVS: Image space based visualization of flow on surfaces. IEEE TVCG 10, 6 (2004), 637-648. (Cited on page 2) Hummel M., Garth C., Hamann B., Hagen H., Joy K.: Iris: Illustrative rendering for integral surfaces. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1319-1328. (Cited on pages 2, 6, and 9) Candelaresi S., Brandenburg A.: Decay of helical and nonhelical magnetic knots. Phys. Rev. E 84, 1 (2011), 16406-16416. (Cited on page 8) Schulze M., Germer T., Rössl C., Theisel H.: Stream surface parametrization by flow-orthogonal front lines. CGF (Proc. SGP) 31, 5 (2012), 1725-1734. (Cited on pages 2 and 7) Bürger K., Ferstl F., Theisel H., Westermann R.: Interactive streak surface visualization on the GPU. IEEE TVCG (Proc. Vis) 15, 6 (2009), 1259-1266. (Cited on page 7) Sujudi D., Haimes R.: Identification Of Swirling Flow In 3-D Vector Fields. Tech. rep., MIT, 1995. (Cited on page 9) Edmunds M., Laramee R.S., Malki R., Masters I., Croft T.N., Chen G., Zhang E.: Automatic stream surface seeding: A feature-centered approach. CGF (Proc. EuroVis) 31, 3 (2012), 1095-1104. (Cited on pages 2, 7, and 9) Weinkauf T., Hege H.-C., Theisel H.: Advected tangent curves: A general scheme for characteristic curves of flow fields. CGF (Proc. Eurographics) 31, 2 (2012), 825-834. (Cited on page 9) Born S., Wiebel A., Friedrich J., Scheuermann G., Bartz D.: Illustrative stream surfaces. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1329-1338. (Cited on page 2) Saito T., Toriwaki J.-I.: New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognition 27, 11 (1994), 1551-1565. (Cited on page 4) Günther T., Schulze M., Martinez Esturo J., Rössl C., Theisel H.: Opacity optimization for surfaces. CGF (Proc. EuroVis) 33, 3 (2014), (to appear). (Cited on pages 2 and 9) 2010; 16 2012 2011 2010 2011; 84 2009 1997 1995 2005 1994; 27 1993 2004 1992 2003 2012; 36 2012; 31 2013; 19 2004; 10 1983; 220 2013; 32 2010; 29 2013 2008; 40 2005; 11 2009; 15 2014; 33 e_1_2_10_22_2 e_1_2_10_23_2 Bürger K. (e_1_2_10_2_2) 2009; 15 Günther T. (e_1_2_10_15_2) 2014; 33 Krüger J. (e_1_2_10_20_2) 2005; 11 Carnecky R. (e_1_2_10_6_2) 2013; 19 Hummel M. (e_1_2_10_16_2) 2010; 16 e_1_2_10_19_2 e_1_2_10_17_2 e_1_2_10_18_2 e_1_2_10_5_2 e_1_2_10_4_2 e_1_2_10_37_2 e_1_2_10_7_2 e_1_2_10_13_2 e_1_2_10_14_2 e_1_2_10_35_2 e_1_2_10_9_2 e_1_2_10_11_2 e_1_2_10_34_2 e_1_2_10_8_2 e_1_2_10_12_2 e_1_2_10_33_2 e_1_2_10_32_2 e_1_2_10_31_2 e_1_2_10_30_2 Born S. (e_1_2_10_3_2) 2010; 16 Edmunds M. (e_1_2_10_10_2) 2012; 36 Weinkauf T. (e_1_2_10_36_2) 2010; 16 e_1_2_10_28_2 e_1_2_10_29_2 e_1_2_10_26_2 Laramee R.S. (e_1_2_10_21_2) 2004; 10 e_1_2_10_27_2 e_1_2_10_24_2 e_1_2_10_25_2
References_xml	– reference: Krüger J., Kipfer P., Kondratieva P., Westermann R.: A particle system for interactive visualization of 3D flows. IEEE TVCG 11, 6 (2005), 744-756. (Cited on page 2) – reference: Sabin M.: Analysis and Design of Univariate Subdivision Schemes. Springer, 2010. (Cited on page 6) – reference: Bürger K., Ferstl F., Theisel H., Westermann R.: Interactive streak surface visualization on the GPU. IEEE TVCG (Proc. Vis) 15, 6 (2009), 1259-1266. (Cited on page 7) – reference: Born S., Wiebel A., Friedrich J., Scheuermann G., Bartz D.: Illustrative stream surfaces. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1329-1338. (Cited on page 2) – reference: Martinez Esturo J., Schulze M., Rössl C., Theisel H.: Global selection of stream surfaces. CGF (Proc. EG) 32, 2 (2013), 113-122. (Cited on pages 1, 2, 3, 4, 6, 7, 8, and 9) – reference: Günther T., Schulze M., Martinez Esturo J., Rössl C., Theisel H.: Opacity optimization for surfaces. CGF (Proc. EuroVis) 33, 3 (2014), (to appear). (Cited on pages 2 and 9) – reference: McLoughlin T., Laramee R.S., Peikert R., Post F.H., Chen M.: Over two decades of integration-based, geometric flow visualization. CGF 29, 6 (2010), 1807-1829. (Cited on pages 2 and 9) – reference: Fabbri R., Costa L. D. F., Torelli J.C., Bruno O.M.: 2D euclidean distance transform algorithms: A comparative survey. ACM Comput. Surv. 40, 1 (2008), 2:1-2:44. (Cited on page 4) – reference: Weinkauf T., Theisel H.: Streak lines as tangent curves of a derived vector field. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1225-1234. (Cited on page 9) – reference: Schulze M., Germer T., Rössl C., Theisel H.: Stream surface parametrization by flow-orthogonal front lines. CGF (Proc. SGP) 31, 5 (2012), 1725-1734. (Cited on pages 2 and 7) – reference: Weinkauf T., Hege H.-C., Theisel H.: Advected tangent curves: A general scheme for characteristic curves of flow fields. CGF (Proc. Eurographics) 31, 2 (2012), 825-834. (Cited on page 9) – reference: Laramee R.S., van Wijk J.J., Jobard B., Hauser H.: ISA and IBFVS: Image space based visualization of flow on surfaces. IEEE TVCG 10, 6 (2004), 637-648. (Cited on page 2) – reference: Hummel M., Garth C., Hamann B., Hagen H., Joy K.: Iris: Illustrative rendering for integral surfaces. IEEE TVCG (Proc. Vis) 16, 6 (2010), 1319-1328. (Cited on pages 2, 6, and 9) – reference: Sujudi D., Haimes R.: Identification Of Swirling Flow In 3-D Vector Fields. Tech. rep., MIT, 1995. (Cited on page 9) – reference: Carnecky R., Fuchs R., Mehl S., Jang Y., Peikert R.: Smart transparency for illustrative visualization of complex flow surfaces. IEEE TVCG 19, 5 (2013), 838-851. (Cited on page 2) – reference: Edmunds M., Laramee R.S., Chen G., Max N., Zhang E., Ware C.: Surface-based flow visualization. C&G 36, 8 (2012), 974-990. (Cited on pages 1 and 2) – reference: Edmunds M., Laramee R.S., Malki R., Masters I., Croft T.N., Chen G., Zhang E.: Automatic stream surface seeding: A feature-centered approach. CGF (Proc. EuroVis) 31, 3 (2012), 1095-1104. (Cited on pages 2, 7, and 9) – reference: Saito T., Toriwaki J.-I.: New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognition 27, 11 (1994), 1551-1565. (Cited on page 4) – reference: Candelaresi S., Brandenburg A.: Decay of helical and nonhelical magnetic knots. Phys. Rev. E 84, 1 (2011), 16406-16416. (Cited on page 8) – reference: Günther T., Rössl C., Theisel H.: Opacity optimization for 3D line fields. ACM TOG (Proc. SIGGRAPH) 32, 4 (2013), 120-120. (Cited on pages 6 and 7) – reference: Kirkpatrick S., Gelatt C.D., Vecchi M.P.: Optimization by simulated annealing. Science 220, 4598 (1983), 671-680. 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Snippet	Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in... Stream surfaces are a well-studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in...
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SubjectTerms	3-D graphics Analysis and systems Categories and Subject Descriptors (according to ACM CCS) Computer graphics Computer Science Computer simulation Datalogi Flow visualization Fluids I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Geometric algorithms I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems Image processing systems languages Mathematical analysis Nucleation Optimization SRA - E-Science (SeRC) SRA - E-vetenskap (SeRC) Streams Studies Three dimensional
Title	Sets of Globally Optimal Stream Surfaces for Flow Visualization
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