Visual thinking colin_ware_lectures_2013_4_patterns

  • Published on

  • View

  • Download

Embed Size (px)




<ul><li> 1. Representing Data using Static and Moving Patterns Colin Ware UNH</li></ul> <p> 2. Introduction Finding patterns is key to information visualization. Expert knowledge is about understanding patterns (Flynn effect) Example Queries: We think by making pattern queries on the world Patterns showing groups? Patterns showing structure? When are patterns similar? How should we organize information on the screen?What makes a pattern distinct? 3. The dimensions of space 4. The What Channel Objects, any locationPatterns of patterns Simple features specific locations 5. Patterns Feature heirarchy (learned) Contours and Regions (formed on the fly) 6. V1 processingWare:Vislab:CCOM 7. Texture segmentation (regions) 8. Textures and low level features 9. Interference based on spatial frequency 10. Low level tuning based on feature maps 11. A diagram with same principle 12. Field, Hayes and Hess Contour finding mechanisms 13. Resultsrt = -4.970 + 1.390spl + 0.01699con + 0.654cr + 0.295brspl: Shortest path length con: continuity cr: crossings br: branches1 crossing adds .65 sec 100 deg. adds 1.7 sec 1 crossing == 38 deg. 14. Connectedness Connectedness assumed in Continuity acbd 15. Continuity Visual entities tend to be smooth and continuous abc 16. Continuity in Diagrams Connections using smooth lines ab 17. The mechanisms of line and contourLOC generalized contour findingWare:Vislab:CCOM 18. Closure Closed contours to show set relationshipA B C D 19. Extending the Euler diagram 20. Collins bubble sets 21. More Contours abDirect application to vector field display 22. How to add VS? Asymmetry along path Terminations Some End-Stopped neurons respond only with terminations in the receptive field.Halles little stroaks 1868 23. Modeling V1 and above Dan Pineo 24. Vector Field VisualizationLaidlaw 25. An optimization process (NSF ITR) Define task requirements Advection path perceptio Magnitude perception Direction perception Identify a visualization Method and a paramaterizationPerceptually optimize for Some sub-set of task requirementsStreaklets: A generalized Flow vis technique Human In the LoopCharacterize solutionsActual solutions Guidelines Algorithms Theory 26. Key idea Almost all solutions can be described as being composed of streakletsMag color Mag luminance Mag size (length, width) Mag spacing Orient orient Direction arrow head Direction shape Direction lum change Direction transparency 27. Task: optimize streaklets. (How?) 1) Streaklet design optimized according to theory head to tail, direction cues Modified Jobard and Lefer (Pete Mitchell) 2) Human in the loop optimization Genetic algorithms (NO) Domain experts with a lot of sliders Designers with a lot of sliders 28. Possibilities for Evaluation Direction Magnitude Advection Global pattern Local pattern Nodal points 29. Back to the feature hierarchy 30. Scatter plots: comparing variables 31. Parallel coords vs Generalized draftsmans plot 32. Parallel coord vs gen draftsmans Parallel Gen drafts Each line is a data Dimension All pairwise scatterplots.Results suggest Gen drafts is best Clusters &amp; correlations Holten and van Wijk 33. Symmetry Symmetry create visual wholePrefer Symmetry 34. Symmetry (cont.) Using symmetry to show Similarities between time series data 35. Bivariate maps (texture + color) 36. 3 Channels: Color, Texture, Motion 37. Compare to this!! 38. Scribble exercise 39. The Magic of Line and Contour: Chameleon linesSaul SteinbergSantiago ColtravaWare:Vislab:CCOM 40. Ware:Vislab:CCOM 41. Patterns in Diagrams Patterns applied acbd 42. Visual Grammar of diagrams Entities represented by Discrete objects Attributes: Shape Colors TexturesRelationships represented by Connecting lines or nesting regions 43. Semantics of structure 44. Treemaps and hierarchies Treemaps use areas (size) SP tree Graph Trees use connectivity (structure)ababc f d e g h iwww.smartmoney.coma bcide f gh 45. Top down Bottom up Tunable attention to patterns Contours and regions + Some are automatic Basic to constructive thinking 46. Part II: Patterns in Motion How can we use motion as a display technique? Gestalt principle of common fate 47. Motion as a visual attribute (Common fate) correlation between points: frequency, phase or amplitude Result: phase is most noticeable 48. Motion is Highly Contextual Group moving objects in hierarchical fashion.ab 49. Using Causality to display causality Michottes claim: direct perception of causality 50. A causal graph 51. Michottes Causality Perception10 0%D e t Lu c i g i c a nhn r Dl ydl u c i g ea e a n hn N cua t o a s liy5% 010 0 T e( s c i me .) m20 0 52. Visual Causal Vectors 53. Experiment Evaluate VCVs Symmetry about time of contact. 54. Results Perceived effectCu a r lat n h a s l e io s ip p 1S m re tio s i o e la n hp p 3N r lat n h o e io s ip p 2-1 .00 .5 -0 .5 0 .0 T ere tiv toco ta (se o d im la e n ct c n s)1 .0 55. Motion Patterns that attract attention (Lyn Bartram) Motion is a good attention getter in periphery The optimal pattern may be things that emerge, as opposed to simply move. We may be able to perceive large field patterns better when they are expressed through motion (untested) 56. Anthropomorphic Form from motion Pattern of moving dots (captured from actor body) Johansson. Attach meaning to movements (Heider and Semmel)ab 57. Conclusion Gestalt Laws are useful as design guidelines. Patterns should be present in luminance Patterns should be the appropriate size Motion is under-researched, but evidence suggest its power. Simple motion coding can be used to express communication, causality, urgency, happiness? (Braitenberg) 58. Algorithms Optimizing trace density (poisson disk) Flexible methods for rendering (enhanced particle systems). 59. Figures and Grounds (cont.) Rubins Vase Competing recognition processes 60. Show particle solutions Problem: how do we create an optimal solution out of all of these possibilities?Standard solution: do studies and measure the effect of different parameters.Problem: Too many alternatives. 61. Closure (cont.) Segmenting screen Creating frame of reference Position of objects judged based on enclosing frame. 62. Laciness (Cavanaugh) abLayered data: be careful with composites of textures cd 63. Transparency Continuity is important in transparency x &lt; y &lt; z or x &gt; y &gt; z y &lt; z &lt; w or y &gt; z &gt; wxwyb az 64. Limitation due to Frame Rate Can only show motions that are limited by the Frame Rate. We can increase by using additional </p>