article

Perceptually based brush strokes for nonphotorealistic visualization

Authors:

Christopher G. Healey,

Laura Tateosian,

Mark RempleAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 23, Issue 1

Pages 64 - 96

https://doi.org/10.1145/966131.966135

Published: 01 January 2004 Publication History

Abstract

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.

References

[1]

Aks, D. J. and Enns, J. T. 1996. Visual search for size is influenced by a background texture gradient. J. Experim. Psych.: Human Percept. Perf. 22, 6, 1467--1481.

[2]

Bergman, L. D., Rogowitz, B. E., and Treinish, L. A. 1995. A rule-based tool for assisting colormap selection. In Proceedings of Visualization '95 (Atlanta, Ga.). 118--125.

[3]

Birren, F. 1969. Munsell: A Grammar of Color. Van Nostrand Reinhold Company, New York, New York.

[4]

Brown, R. 1978. Impressionist technique: Pissarro's optical mixture. In Impressionism in Perspective, B. E. White, Ed. Prentice-Hall, Inc., Englewood Cliffs, N. J., 114--121.

[5]

Callaghan, T. C. 1990. Interference and dominance in texture segregation. In Visual Search, D. Brogan, Ed. Taylor & Francis, New York, 81--87.

[6]

Chevreul, M. E. 1967. The Principles of Harmony and Contrast of Colors and Their Applications to the Arts. Reinhold Publishing Corporation, New York.

[7]

CIE. 1978. CIE Publication No. 15, Supplement Number 2 (E-1.3.1, 1971): Official Recommendations on Uniform Color Spaces, Color-Difference Equations, and Metric Color Terms. Commission Internationale de L'Èclairge.

[8]

Coren, S., Ward, L. M., and Enns, J. T. 2003. Sensation and Perception (6th Edition). Wiley, New York, New York.

[9]

Curtis, C. J., Anderson, S. E., Seims, J. E., Fleischer, K. W., and Salesin, D. H. 1997. Computer-generated watercolor. In SIGGRAPH 97 Conference Proceedings (Los Angeles, Calif.) T. Whitted, Ed. ACM, New York, 421--430.

[10]

Cutting, J. E. and Millard, R. T. 1984. Three gradients and the perception of flat and curved surfaces. J. Experiment. Psych.: General 113, 2, 198--216.

[11]

Ebert, D. and Rheingans, P. 2000. Volume illustration: Non-photorealistic rendering of volume models. In Proceedings of Visualization 2000 (San Francisco, Calif.). 195--202.

[12]

Egeth, H. E. and Yantis, S. 1997. Visual attention: Control, representation, and time course. Ann. Rev. Psychol. 48, 269--297.

[13]

Finkelstein, A. and Salesin, D. H. 1994. Multiresolution curves. In SIGGRAPH 94 Conference Proceedings (Orlando, Fla.), A. S. Glassner, Ed. ACM, New York, 261--268.

[14]

Gooch, B., Coombe, G., and Shirley, P. 2002. Artistic vision: Painterly rendering using computer vision techniques. In Proceedings of the NPAR 2002 Symposium on Non-Photorealistic Animation and Rendering (Annecy, France). 83--90.

[15]

Gooch, B. and Gooch, A. 2001. Non-Photorealistic Rendering. A K Peters, Ltd., Natick, Mass.

[16]

Grinstein, G., Pickett, R., and Williams, M. 1989. EXVIS: An exploratory data visualization environment. In Proceedings of Graphics Interface '89 (London, Ont., Canada). 254--261.

[17]

Haberli, P. 1990. Paint by numbers: Abstract image representations. Comput. Graph. (SIGGRAPH 90 Conference Proceedings) 24, 4, 207--214.

[18]

Haberli, P. and Segal, M. 1993. Texture mapping as a fundamental drawing primative. In Proceedings of the 4th Eurographics Workshop on Rendering, (Paris, France). M. Cohen, C. Puech, and F. Sillion, Eds. 259--266.

[19]

Haralick, R. M., Shanmugam, K., and Dinstein, I. 1973. Textural features for image classification. IEEE Trans. Syst., Man, and Cybernet. SMC-3, 6, 610--621.

[20]

Healey, C. G. 1996. Choosing effective colors for data visualization. In Proceedings of Visualization '96 (San Francisco, Calif.). 263--270.

[21]

Healey, C. G., Booth, K. S., and Enns, J. T. 1996. High-speed visual estimation using preattentive processing. ACM Trans. Computer-Hum. Interact. 3, 2, 107--135.

[22]

Healey, C. G. and Enns, J. T. 1998. Building perceptual textures to visualize multidimensional datasets. In Proceedings of Visualization '98 (Research Triangle Park, N. C.). 111--118.

[23]

Healey, C. G. and Enns, J. T. 1999. Large datasets at a glance: Combining textures and colors in scientific visualization. IEEE Trans. Visual. Comput. Graph. 5, 2, 145--167.

[24]

Hering, E. 1964. Outlines of a Theory of Light Sense. Harvard University Press, Cambridge, Mass.

[25]

Hertzmann, A. 1998. Painterly rendering with curved brush strokes of multiple sizes. In SIGGRAPH 98 Conference Proceedings (Orlando, Fla.). M. Cohen, Ed. ACM, New York, 453--460.

[26]

Hertzmann, A. 2002. Fast texture maps. In Proceedings of NPAR 2002 Symposium on Non-Photorealistic Animation and Rendering (Annecy, France). 91--96.

[27]

Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. H. 2001. Image analogies. In SIGGRAPH 2001 Conference Proceedings (Los Angeles, Calif.). E. Fiume, Ed. ACM, New York, 327--340.

[28]

Hsu, S. C. and Lee, I. H. H. 1994. Drawing and animation using skeletal strokes. In SIGGRAPH 94 Conference Proceedings (Orlando, Fla.). A. Glassner, Ed. ACM, New York, 109--118.

[29]

Interrante, V. 2000. Harnessing natural textures for multivariate visualization. IEEE Comput. Graph. Applic. 20, 6, 6--11.

[30]

Julész, B. 1975. Experiments in the visual perception of texture. Scient. Amer. 232, 34--43.

[31]

Julész, B. 1984. A brief outline of the texton theory of human vision. Trends Neurosci. 7, 2, 41--45.

[32]

Julész, B., Gilbert, E. N., and Shepp, L. A. 1973. Inability of humans to discriminate between visual textures that agree in second-order statistics---revisited. Perception 2, 391--405.

[33]

Julész, B., Gilbert, E. N., and Victor, J. D. 1978. Visual discrimination of textures with identical third-order statistics. Biologic. Cybernet. 31, 137--140.

[34]

Kirby, R. M., Marmanis, H., and Laidlaw, D. H. 1999. Visualizing multivalued data from 2D incompressible flows using concepts from painting. In Proceedings of Visualization '99 (San Francisco, Calif.). 333--340.

[35]

Laidlaw, D. H. 2001. Loose, artistic "textures" for visualization. IEEE Comput. Graph. Applic. 21, 2, 6--9.

[36]

Laidlaw, D. H., Ahrens, E. T., Kremers, D., Avalos, M. J., Jacobs, R. E., and Readhead, C. 1998. Visualizing diffusion tensor images of the mouse spinal cord. In Proceedings of Visualization '98 (Research Triangle Park, N. C.). 127--134.

[37]

Lewis, J.-P. 1984. Texture synthesis for digital painting. Computer Graphics (SIGGRAPH 84 Proceedings) 18, 3, 245--252.

[38]

Litwinowicz, P. 1997. Processing images and video for an impressionist effect. In SIGGRAPH 97 Conference Proceedings, (Los Angeles, Calif.). T. Whitted, Ed. ACM, New York, 407--414.

[39]

Liu, G., Healey, C. G., and Enns, J. T. 2003. Target detection and localization in visual search: A dual systems perspective. Percept. Psychophys. 65, 5, 678--694.

[40]

Lu, A., Morris, C. J., Ebert, D. S., Rheingans, P., and Hansen, C. 2002. Non-photorealistic volume rendering using stippling techniques. In Proceedings of Visualization 2002 (Boston, Mass.), 211--218.

[41]

MacEachren, A. M. 1995. How Maps Work. Guilford Publications, Inc., New York.

[42]

Mack, A. and Rock, I. 1998. Inattentional Blindness. MIT Press, Menlo Park, Calif.

[43]

McCormick, B. H., DeFanti, T. A., and Brown, M. D. 1987. Visualization in scientific computing. Comput. Graph. 21, 6, 1--14.

[44]

Meier, B. J. 1996. Painterly rendering for animation. In SIGGRAPH 96 Conference Proceedings (New Orleans, La.) H. Rushmeier, Ed. ACM, New York, 477--484.

[45]

Munsell, A. H. 1905. A Color Notation. Geo. H. Ellis Co., Boston, Mass.

[46]

Pomerantz, J. and Pristach, E. A. 1989. Emergent features, attention, and perceptual glue in visual form perception. J. Experim. Psych.: Human Percept. Perf. 15, 4, 635--649.

[47]

Posner, M. I. and Raichle, M. E. 1994. Images of mind. Scientific American Library.

[48]

Ramachandran, V. S. and Hirstein, W. 1999. The science of art: A neurological theory of aesthetic experience. J. Conscious. Stud. 6, 6-7, 15--51.

[49]

Rao, A. R. and Lohse, G. L. 1993a. Identifying high level features of texture perception. CVGIP: Graph. Models Image Process. 55, 3, 218--233.

[50]

Rao, A. R. and Lohse, G. L. 1993b. Towards a texture naming system: Identifying relevant dimensions of texture. In Proceedings of Visualization '93 (San Jose, Calif.), 220--227.

[51]

Rensink, R. A. 2000. Seeing, sensing, and scrutinizing. Vision Res. 40, 10--12, 1469--1487.

[52]

Rheingans, P. and Ebert, D. 2001. Volume illustration: Nonphotorealistic rendering of volume models. IEEE Trans. Vis. Comput. Graph. 7, 3, 253--264.

[53]

Rheingans, P. and Tebbs, B. 1990. A tool for dynamic explorations of color mappings. Comput. Graph. 24, 2, 145--146.

[54]

Rogowitz, B. E. and Treinish, L. A. 1993. An architecture for rule-based visualization. In Proceedings of Visualization '93 (San Jose, Calif.), 236--243.

[55]

Rood, O. N. 1879. Modern Chromatics, with Applications to Art and Industry. Appleton, New York.

[56]

Rosenblum, L. J. 1994. Research issues in scientific visualization. IEEE Comput. Graph. Applic. 14, 2, 61--85.

[57]

Salisbury, M., Anderson, C., Lischinski, D., and Salesin, D. H. 1996. Scale-dependent reproduction of pen-and-ink illustrations. In SIGGRAPH 96 Conference Proceedings (New Orleans, La), H. Rushmeier, Ed. ACM, New York, 461--468.

[58]

Salisbury, M., Anderson, S. E., Barzel, R., and Salesin, D. H. 1994. Interactive pen-and-ink illustrations. In SIGGRAPH 94 Conference Proceedings, (Orlando, Fla.). A. S. Glassner, Ed. ACM, New York, 101--108.

[59]

Salisbury, M., Wong, M. T., Hughes, J. F., and Salesin, D. H. 1997. Orientable textures for image-based pen-and-ink illustration. In SIGGRAPH 97 Conference Proceedings (Los Angeles, Calif.). T. Whitted, Ed. 401--406.

[60]

Schapiro, M. 1997. Impressionism: Reflections and Perceptions. George Brazillier, Inc., New York.

[61]

Shiraishi, M. and Yamaguchi, Y. 1999. Image moment-based stroke placement. In SIGGRAPH 99: Sketches & Applications (Los Angeles, Calif.). R. Kidd, Ed. ACM, New York, 247.

[62]

Simons, D. J. 2000. Current approaches to change blindness. Vis. Cognit. 7, 1/2/3, 1--15.

[63]

Slocum, T. A. 1998. Thematic Cartography and Visualization. Prentice-Hall, Inc., Upper Saddle River, N. J.

[64]

Smith, P. H. and Van Rosendale, J. 1998. Data and visualization corridors report on the 1998 CVD workshop series (sponsored by DOE and NSF). Tech. Rep. CACR-164, Center for Advanced Computing Research, California Institute of Technology.

[65]

Snowden, R. J. 1998. Texture segregation and visual search: A comparison of the effects of random variations along irrelevant dimensions. J. Experiment. Psych.: Human Percept. Perform. 24, 5, 1354--1367.

[66]

Sousa, M. C. and Buchanan, J. W. 1999a. Computer-generated graphite pencil rendering of 3d polygon models. Comput. Graph. Forum (Proceedings Eurographics '99) 18, 3, 195--208.

[67]

Sousa, M. C. and Buchanan, J. W. 1999b. Computer-generated pencil drawings. In Proceedings SKIGRAPH '99 (Banff, Canada).

[68]

Sousa, M. C. and Buchanan, J. W. 2000. Observational models of graphite pencil materials. Comput. Graph. Forum 19, 1, 27--49.

[69]

Strassmann, S. 1986. Hairy brushes. Comput. Graph. (SIGGRAPH 86 Proceedings) 20, 4, 185--194.

[70]

Strothotte, T. and Schlechtweg, S. 2002. Non-Photorealistic Computer Graphics: Modeling, Rendering and Animation. Morgan-Kaufmann, Inc., San Francisco, Calif.

[71]

Takagi, S. and Fujishiro, I. 1997. Microscopic structural modeling of colored pencil drawings. In SIGGRAPH 97 Sketches & Applications (Los Angeles, Calif.). D. S. Ebert, Ed. ACM, New York, 187.

[72]

Takagi, S., Fujishiro, I., and Nakajima, M. 1999. Volumetric modeling of artistic techniques in colored pencil drawing. In SIGGRAPH 99 Sketches & Applications, (Los Angeles, Calif.). R. Kidd, Ed. ACM, New York, 283.

[73]

Tamura, H., Mori, S., and Yamawaki, T. 1978. Textural features corresponding to visual perception. IEEE Trans. Syst. Man, and Cybernet. SMC-8, 6, 460--473.

[74]

Triesman, A. 1985. Preattentive processing in vision. Comput. Vis. Graph. Image Process. 31, 156--177.

[75]

Triesman, A. 1991. Search, similarity, and integration of features between and within dimensions. J. Experiment. Psych.: Human Percept. Perf. 17, 3, 652--676.

[76]

Triesman, A. and Gormican, S. 1988. Feature analysis in early vision: Evidence from search asymmetries. Psychol. Rev. 95, 1, 15--48.

[77]

Tufte, E. R. 1983. The Visual Display of Quantitative Information. Graphics Press, Cheshire, Conn.

[78]

Tufte, E. R. 1990. Envisioning Information. Graphics Press, Cheshire, Conn.

[79]

Tufte, E. R. 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire, Conn.

[80]

Venturi, L. 1978. Impressionist style. In Impressionism in Perspective, B. E. White, Ed. Prentice-Hall, Inc., Englewood Cliffs, N. J., 105--113.

[81]

Ware, C. 1988. Color sequences for univariate maps: Theory, experiments, and principles. IEEE Comput. Graph. Applic. 8, 5, 41--49.

[82]

Ware, C. 2000. Information Visualization: Perception for Design. Morgan-Kaufmann, San Francisco, Calif.

[83]

Ware, C. and Knight, W. 1995. Using visual texture for information display. ACM Trans. Graph. 14, 1, 3--20.

[84]

Weigle, C., Emigh, W. G., Liu, G., Taylor, R. M., Enns, J. T., and Healey, C. G. 2000. Oriented texture slivers: A technique for local value estimation of multiple scalar fields. In Proceedings of Graphics Interface 2000 (Montréal, Quebec, Canada), 163--170.

[85]

Winkenbach, G. and Salesin, D. H. 1994. Computer-generated pen-and-ink illustration. In SIGGRAPH 94 Conference Proceedings. (Orlando, Fla.). A. Glassner, Ed. ACM, New York, 91--100.

[86]

Winkenbach, G. and Salesin, D. H. 1996. Rendering free-form surfaces in pen-and-ink. In SIGGRAPH 96 Conference Proceedings, (New Orleans, La.). H. Rushmeier, Ed. ACM, New York, 469--476.

[87]

Wolfe, J. M. 1994. Guided Search 2.0: A revised model of visual search. Psycho. Bull. Rev. 1, 2, 202--238.

[88]

Wolfe, J. M., Klempen, N., and Dahlen, K. 2000. Post attentive vision. J. Experiment. Psych.: Human Percept. Perform. 26, 2, 693--716.

[89]

Zeki, S. 1999. Inner Vision. Oxford University Press, Oxford, U.K.

Cited By

Arunkumar APadilla LBae GBryan C(2024)Image or Information? Examining the Nature and Impact of Visualization Perceptual ClassificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332691930:1(1030-1040)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326919
Bigerelle MGuibert RMironova ARobache FDeltombe RNys LBrown C(2023)Fractal and statistical characterization of brushstroke on paintingsSurface Topography: Metrology and Properties10.1088/2051-672X/acbe5311:1(015019)Online publication date: 28-Mar-2023
https://doi.org/10.1088/2051-672X/acbe53
Peng NXue CWang HNiu YWu L(2021)Priming Effect of Colour on Aiding the Attentional Reorientation in Sequential Presentations of Temporal Data Visualization: Evidence From Eye-TrackingInteracting with Computers10.1093/iwc/iwab02133:2(188-210)Online publication date: 28-Aug-2021
https://doi.org/10.1093/iwc/iwab021
Show More Cited By

Index Terms

Perceptually based brush strokes for nonphotorealistic visualization

Recommendations

Engaging viewers through nonphotorealistic visualizations
NPAR '07: Proceedings of the 5th international symposium on Non-photorealistic animation and rendering

Research in human visual cognition suggests that beautiful images can engage the visual system, encouraging it to linger in certain locations in an image and absorb subtle details. By developing aesthetically pleasing visualizations of data, we aim to ...
Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization

This paper presents a new method for using texture and color to visualize multivariate data elements arranged on an underlying height field. We combine simple texture patterns with perceptually uniform colors to increase the number of attribute values ...
On Perception of Semi-Transparent Streamlines for Three-Dimensional Flow Visualization

One of the standard techniques to visualize three-dimensional flow is to use geometry primitives. This solution, when opaque primitives are used, results in high levels of occlusion, especially with dense streamline seeding. Using semi-transparent ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 23, Issue 1

January 2004

96 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/966131

Issue’s Table of Contents

Copyright © 2004 ACM.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2004

Published in TOG Volume 23, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

51
Total Citations
View Citations
2,874
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)3

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Arunkumar APadilla LBae GBryan C(2024)Image or Information? Examining the Nature and Impact of Visualization Perceptual ClassificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332691930:1(1030-1040)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326919
Bigerelle MGuibert RMironova ARobache FDeltombe RNys LBrown C(2023)Fractal and statistical characterization of brushstroke on paintingsSurface Topography: Metrology and Properties10.1088/2051-672X/acbe5311:1(015019)Online publication date: 28-Mar-2023
https://doi.org/10.1088/2051-672X/acbe53
Peng NXue CWang HNiu YWu L(2021)Priming Effect of Colour on Aiding the Attentional Reorientation in Sequential Presentations of Temporal Data Visualization: Evidence From Eye-TrackingInteracting with Computers10.1093/iwc/iwab02133:2(188-210)Online publication date: 28-Aug-2021
https://doi.org/10.1093/iwc/iwab021
Anjos RRibeiro CLopes DPereira J(2018)Stroke-based splattingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1420-734:10(1383-1397)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s00371-017-1420-7
Voglreiter PHofmann MEbner CSequeiros RPortugaller HFütterer JMoche MSteinberger MSchmalstieg DHauser HBruckner SPreim BVilanova AHauser HHennemuth ALundervold A(2016)Visualization-guided evaluation of simulated minimally invasive cancer treatmentProceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine10.5555/3061507.3061533(163-172)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.5555/3061507.3061533
Rosin PLai YSezgin MRosin P(2015)Non-photorealistic rendering of portraitsProceedings of the workshop on Computational Aesthetics10.5555/2811239.2811249(159-170)Online publication date: 20-Jun-2015
https://dl.acm.org/doi/10.5555/2811239.2811249
Quispel AMaes ASchilperoord J(2015)Graph and chart aesthetics for experts and laymen in design: The role of familiarity and perceived ease of useInformation Visualization10.1177/147387161560647815:3(238-252)Online publication date: 24-Sep-2015
https://doi.org/10.1177/1473871615606478
Schroeder DKeefe D(2015)Visualization-by-Sketching: An Artist's Interface for Creating Multivariate Time-Varying Data VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2015.246715322:1(877-885)Online publication date: 27-Oct-2015
https://dl.acm.org/doi/10.1109/TVCG.2015.2467153
Steed CSwan JFitzpatrick PJankun-Kelly T(2014)A Visual Analytics Approach for Correlation, Classification, and Regression AnalysisInnovative Approaches of Data Visualization and Visual Analytics10.4018/978-1-4666-4309-3.ch002(25-45)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-4309-3.ch002
Chi D(2014)A natural image pointillism with controlled ellipse dotsAdvances in Multimedia10.1155/2014/5678462014(17-17)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1155/2014/567846
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents