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Texturing of Layered Surfaces Texturing of Layered Surfaces for Optimal Viewingfor Optimal Viewing
Alethea Bair, Texas A&M UniversityAlethea Bair, Texas A&M University
Donald House, Texas A&M UniversityDonald House, Texas A&M University
Colin Ware, University of New Colin Ware, University of New HampshireHampshire
Outline
3. Data Analysis 4. Follow Up Study
1. Previous Work 2. Experiment
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.
Introduction
Introduction
Introduction
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Previous Work
• Human-in-the-loop Method:
House, Bair, Ware. On the Optimization of Visualizations of Complex Phenomena, VIS 2005.
Issues with 2005 Experiment
• Complicated textures• Fixed large-scale
surface features• Subjective rating• Slow convergence• Resolution lower
than human eye• Stereo glasses
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
• Reduced parameters from 122 to 26– Grid layout– Size– Aspect ratio– Randomness– Color– Brightness– Roundness– Blur– Orientation– Opacity
Texture Parameterization
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.
QuickTime™ and aDV/DVCPRO - NTSC decompressor
are needed to see this picture.
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.
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.
Wheatstone Stereoscope
Stereoscope Resolution
Screens had a resolution of 3840 x 2400
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
ANOVA
• Shows the significance of an individual parameter’s effect on the rating.
median
1 quartile
1.5 quartile
outlier+
Linear Discriminant Analysis
• Determines parameter vectors that best separate good from bad visualizations.
Decision Tree Analysis
• Determines the best parameter settings to classify visualizations by ratings.
Parallel Coordinate Analysis
• Used to visually identify parameter trends
Lines colored by top opacity
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
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.
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.
Experimental Results
• Agreement with predicted ratings.– Box plots show the distribution of ratings.
Losers!
Rating 1.05 Rating 2.6
Winners!
Rating 8.14 Rating 7.87
Conclusions
QuickTime™ and aDV/DVCPRO - NTSC decompressor
are needed to see this picture.
Future Work
• Surface conforming textures
• Exhaustive experiments in a constrained space
• Printed media
Acknowledgments
• National Science Foundation
• Center for Coastal and Ocean Mapping, University of New Hampshire
• Visualization Laboratory, Texas A&M University
• 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
Recap of Guidelines:
Search Results
• 6 subjects
• 4560 different rated visualizations
Random Final Database
Genetic Algorithm