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