Visual thinking colin_ware_lectures_2013_3_findability
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Visual thinking colin_ware_lectures_2013_3_findability
- 1. Basic Vision+ The process and what stands out CH1 – CH2 + supplimentary Ware:Vislab:CCOM
- 3. Arctic Sea Ice Volume (1D Scalar) 17.5K ~3K Ware:Vislab:CCOM
- 4. Data Classes Scalar Field • 1D • 2D • 3D Vector Field • 1D • 2D • 3D Time Varying • All of the above What is a map? Discrete • 1D • K-dimensional Nominal • Overlapping sets of data with multiple nominal attributes Graph G = {V,E} • • • • Tree Directed acyclic Weighted graph Hyper graph Ware:Vislab:CCOM
- 7. 1 diopter: a lens that focuses at 1 meter. 45d Cornea ~15d Lens d = 1/f
- 8. Acuities Vernier super acuity (10 sec) Grating acuity Two Point acuity (0.5 min) What is the ideal resolution
- 9. CAVE iPhone 960x640 5 cm 1280x1280 Resolution problems Light scattering problems
- 10. CAVE iPhone 960x640 5 cm 128 pixels/degree 1280x1280 16,000 pixels/ deg2 14 pixels/deg 196 pixels/ deg2 Resolution problems Light scattering problems
- 11. Immersion VR HMD + head tracking Data glove
- 13. Cutoff at 50 cycles/deg. Receptors: 20 sec of arc Pooled over larger and larger areas 100 million receptors 1 million fibers to brain A desktop screen may have 30 pixels/cm – need about 4 times as much. I phones better. VR displays have 5 pixels/cm
- 14. Aliasing and Anti aliasing Input pattern Pixel matrix Output pattern
- 15. Temporal Aliasing Human Flicker fusion 50 Hz Temporal aliasing occurs with moving targets Must compute motion blur to fix the problem
- 17. Brain Pixels
- 18. Ware:Vislab:CCOM
- 20. Ware:Vislab:CCOM
- 21. Visual search U fu V al se l isu F ldo V ie f iew V a isu l Sa o e rch r M n rin o ito g S te y tra g Ee y Mv m t o e en Cn l o tro
- 22. The Process of Visual Thinking Ware:Vislab:CCOM
- 23. Spotfire product
- 24. What we can easily find :CH2 Visual search depends on • Salience (low level properties) • Tuning of low level properties • Search skills (knowledge) Ware:Vislab:CCOM
- 28. Color is Pre-Attentive (Pops out) 8 9 0 9 72 5 2 9 0 9 4 9 70 9 7 5 2 0 6 6 84 3775 9 9 0 8 0 30 9 - 8 8 0 8 0 8 58 0 0 8 0 8 0 0 28 258 28 0 5 4 8 t 82 7 2 6 7 8 y 2 95 0 7 2 7 4 0 4 70 8925 0 7 2 9 8 4 95 9 1 1 8 9 5 0 02 8 7 4 0 5 4 9 28 83r7 6 7 8 9 7 8 99 399 9 4 9 5 2 59 0 770557 2 5928 4 7
- 29. Generic Pre-Attentive Experiment Number of irrelevant items varies Pre-attentive 10 msec per item or better. 900 700 500 3 6 12 Number of distractors
- 30. Color
- 32. Orientation
- 34. Motion
- 36. Size
- 38. Simple shading
- 40. Conjunction (does not pop out)
- 42. Semantic Depth of Field
- 44. Relevance Symbol design (nominal coding) Highlighting, - search guidance. Glyph design (quantititive symbols) Ware:Vislab:CCOM
- 45. Mapping data to display variables Data glyphs Position (2) Orientation (1) Size (spatial frequency) Motion (2)++ Blinking? Color (3) Note we have the problem of heterogeneity – There is no good solution Star glyph Method
- 46. Starplot glyph
- 48. Surplus Data into Art? Ware:Vislab:CCOM
- 50. Redundant coding Outliers on feature dimensions are more easily seen Ware:Vislab:CCOM
- 51. A symbol set designed for rapid search Ware:Vislab:CCOM
- 52. Asymmetries in search Add something rather than subtract something Highlighting methods Ware:Vislab:CCOM
- 53. Motion Highlighting We are very sensitive to motion in the periphery. (Peterson and Dugas, 1972) Ware:Vislab:CCOM
- 54. How to make a reminder -emergence (Hillstrom and Yanti, 1994) Ware:Vislab:CCOM
- 55. Search as a process Ware:Vislab:CCOM
- 56. Search as a process Ware:Vislab:CCOM