Animated versus static views of steady flow patterns

Two experiments were conducted to test the hypothesis that animated representations of vector fields are more effective than common static representations even for steady flow. We compared four flow visualization methods: animated streamlets, animated orthogonal line segments (where short lines were elongated orthogonal to the flow direction but animated in the direction of flow), static equally spaced streamlines, and static arrow grids. The first experiment involved a pattern detection task in which the participant searched for an anomalous flow pattern in a field of similar patterns. The results showed that both the animation methods produced more accurate and faster responses. The second experiment involved mentally tracing an advection path from a central dot in the flow field and marking where the path would cross the boundary of a surrounding circle. For this task the animated streamlets resulted in better performance than the other methods, but the animated orthogonal particles resulted in the worst performance. We conclude with recommendations for the representation of steady flow patterns.

[1]  Colin Ware,et al.  Toward a Perceptual Theory of Flow Visualization , 2008, IEEE Computer Graphics and Applications.

[2]  D. Simons,et al.  Moving and looming stimuli capture attention , 2003, Perception & psychophysics.

[3]  Michael S. Langer,et al.  Perceptual limits on 2D motion-field visualization , 2006, TAP.

[4]  Colin Ware,et al.  Improving the Display of Wind Patterns and Ocean Currents , 2014 .

[5]  Colin Ware,et al.  Neural modeling of flow rendering effectiveness , 2008, TAP.

[6]  Anne Treisman,et al.  Preattentive processing in vision , 1985, Computer Vision Graphics and Image Processing.

[7]  Colin Ware,et al.  Representing Flow Patterns by Using Streamlines with Glyphs , 2013, IEEE Transactions on Visualization and Computer Graphics.

[8]  T. J. Jankun-Kelly,et al.  A 2D Flow Visualization User Study Using Explicit Flow Synthesis and Implicit Task Design , 2012, IEEE Transactions on Visualization and Computer Graphics.

[9]  Constance S. Royden,et al.  Visual search asymmetries in motion and optic flow fields , 2001, Perception & psychophysics.

[10]  Jacques Bertin,et al.  Semiology of Graphics - Diagrams, Networks, Maps , 2010 .

[11]  John J. Bertin,et al.  The semiology of graphics , 1983 .

[12]  Nelson Max,et al.  Flow visualization using moving textures , 1995 .

[13]  Alexandru Telea,et al.  Simplified representation of vector fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[14]  Christopher G. Healey,et al.  Visualizing data with motion , 2005, VIS 05. IEEE Visualization, 2005..

[15]  Brian Cabral,et al.  Imaging vector fields using line integral convolution , 1993, SIGGRAPH.

[16]  L. Stone,et al.  Speed tuning of motion segmentation and discrimination , 1999, Vision Research.

[17]  M. Wertheimer.  Laws of organization in perceptual forms. , 1938 .

[18]  Wilfrid Lefer,et al.  High-quality animation of 2D steady vector fields , 2004, IEEE Transactions on Visualization and Computer Graphics.

[19]  David S. Ebert,et al.  Visualization and computer graphics , 2007 .

[20]  Thomas W. Calvert,et al.  Moticons: : detection, distraction and task , 2003, Int. J. Hum. Comput. Stud..

[21]  J. Wolfe,et al.  What attributes guide the deployment of visual attention and how do they do it? , 2004, Nature Reviews Neuroscience.

[22]  Robert Michael Kirby,et al.  Comparing 2D vector field visualization methods: a user study , 2005, IEEE Transactions on Visualization and Computer Graphics.

[23]  Jon Driver,et al.  Visual search for a conjunction of movement and form is parallel , 1988, Nature.

[24]  Suyash P. Awate,et al.  Computer Vision, Graphics, and Image Processing , 2016, Lecture Notes in Computer Science.

[25]  Daniel F. Keefe,et al.  Particle flurries , 2004, IEEE Computer Graphics and Applications.

[26]  Daniel Weiskopf,et al.  Animation of Orthogonal Texture Patterns for Vector Field Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[27]  Dennis R. Wixon,et al.  CHI '02 Extended Abstracts on Human Factors in Computing Systems , 2002, CHI 2002.

[28]  J. V. van Wijk.  Flow visualization with surface particles , 1993, IEEE Computer Graphics and Applications.

[29]  Colin Ware,et al.  Designing a better weather display , 2013, Inf. Vis..

[30]  Eduard Gröller,et al.  Color-Table Animation of Fast Oriented Line Integral Convolution for Vector Field Visualization , 2000, WSCG.

[31]  Wilfrid Lefer,et al.  The motion map: efficient computation of steady flow animations , 1997 .

[32]  Robert S. Laramee,et al.  The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques , 2004, Comput. Graph. Forum.

[33]  Robert S. lramee,et al.  Interactive 3D flow visualization using a streamrunner , 2002, CHI Extended Abstracts.

[34]  W. Newsome,et al.  A selective impairment of motion perception following lesions of the middle temporal visual area (MT) , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[35]  H. Nothdurft.  The role of features in preattentive vision: Comparison of orientation, motion and color cues , 1993, Vision Research.

[36]  Wilfrid Lefer,et al.  The motion map: efficient computation of steady flow animations , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[37]  DH Hubel,et al.  Psychophysical evidence for separate channels for the perception of form, color, movement, and depth , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[38]  David Whitney,et al.  How Capacity Limits of Attention Influence Information Visualization Effectiveness , 2012, IEEE Transactions on Visualization and Computer Graphics.