1 / 48

Texturing of Layered Surfaces for Optimal Viewing

Texturing of Layered Surfaces for Optimal Viewing. Alethea Bair, Texas A&M University Donald House, Texas A&M University Colin Ware, University of New Hampshire. Outline. 1. Previous Work 2. Experiment. 3. Data Analysis 4. Follow Up Study. Introduction. Problem:

yin
Download Presentation

Texturing of Layered Surfaces for Optimal Viewing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Texturing of Layered Surfaces for Optimal Viewing Alethea Bair, Texas A&M University Donald House, Texas A&M University Colin Ware, University of New Hampshire

  2. Outline 1. Previous Work 2. Experiment 3. Data Analysis 4. Follow Up Study

  3. Introduction • Problem: • Display layered surfaces. • Goal: • Maximize shape perception. • Texture has been shown to aid shape perception on a single surface. • But textures interact across 2 surfaces.

  4. Introduction

  5. Introduction

  6. Introduction

  7. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  8. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  9. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  10. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  11. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  12. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  13. Previous Work • Human-in-the-loop Method: House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.

  14. Issues with 2005 Experiment • Complicated textures • Fixed large-scale surface features • Subjective rating • Slow convergence • Resolution lowerthan human eye • Stereo glasses

  15. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  16. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  17. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  18. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  19. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  20. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  21. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  22. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  23. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  24. Texture Parameterization • Reduced parameters from 122 to 26 • Grid layout • Size • Aspect ratio • Randomness • Color • Brightness • Roundness • Blur • Orientation • Opacity

  25. Surface Generation • Surfaces have randomized, multi-scale features • Fractal-like cosine height fields • period varied from 50% to 1% of screen width. • 7 Gaussian bumps • bumps varied from 8% to 2% of screen width.

  26. Rating Method • Rating objectivity improved. • Subjects gave 2 ratings of 0-9, one for each surface. • The rating was based on how well the subject could see all 7 bumps. • A combined rating was the product of the top and bottom surface ratings.

  27. Speeding Human-in-the-Loop Evaluation • Genetic algorithm was modified using islanding • Subjects chose an excellent texture pair • A generation of highly-similar textures was produced around the subject’s choice. • Time for a trial was reduced from 3 hours to 1 hour.

  28. Wheatstone Stereoscope

  29. Stereoscope Resolution Screens had a resolution of 3840 x 2400

  30. Data Analysis Approach • 6 subjects rated 4560 visualizations • We derived guidelines from various data-mining techniques. • For this experiment, we used: • ANOVA • LDA • Decision Trees • Parallel Coordinates

  31. ANOVA • Shows the significance of an individual parameter’s effect on the rating. median 1 quartile 1.5 quartile outlier +

  32. Linear Discriminant Analysis • Determines parameter vectors that best separate good from bad visualizations.

  33. Decision Tree Analysis • Determines the best parameter settings to classify visualizations by ratings.

  34. Parallel Coordinate Analysis • Used to visually identify parameter trends Lines colored by top opacity

  35. Guidelines for Texture Design • Bright top, and brighter bottom surfaces • Long, thin lines on top • Medium to high randomness • Prominent (large, bright, opaque) marks on top • Subtle (small, low opacity) marks on bottom • Either: • Medium top background opacity with medium-sized top marks or • Low top background opacity with large top marks • Little blur on top, more blur on bottom • Chroma can be freely chosen

  36. Evaluating Guidelines • Experiment Used: • Decision tree rules to generate 29 visualizations: • (bad) 4 with rating 1.15 • (poor) 5 with rating 4.57 • (fair) 10 with rating 5.47 • (good) 10 with rating 8.06 • Parallel coordinate trends to generate 31 more: • (enhanced A) 20 (good + lines and background) • (enhanced B) 11 (good + large lines) • 6 Subjects Rated All 60 Visualizations.

  37. Experimental Results • Subject Agreement • Correlations between subjects were greater than 0.57 for all subject pairings. • This has a p-value less than 0.0001.

  38. Experimental Results • Agreement with predicted ratings. • Box plots show the distribution of ratings.

  39. Losers! Rating 1.05 Rating 2.6

  40. Winners! Rating 8.14 Rating 7.87

  41. Conclusions

  42. Future Work • Surface conforming textures • Exhaustive experiments in a constrained space • Printed media

  43. Acknowledgments • National Science Foundation • Center for Coastal and Ocean Mapping, University of New Hampshire • Visualization Laboratory, Texas A&M University

  44. Recap of Guidelines: • Bright top, and brighter bottom surfaces • Long, thin lines on top • Medium to high randomness • Prominent marks on top • Subtle marks on bottom • Either: • Medium top opacity with medium-sized marks or • Low top opacity with large marks • Little blur on top, more blur on bottom • Color can be freely chosen

  45. Search Results • 6 subjects • 4560 different rated visualizations Final Database Random

  46. Genetic Algorithm

More Related