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

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.

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 subjects 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 parameters 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

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

Losers! Rating 1.05Rating 2.6

Winners! Rating 8.14Rating 7.87

Conclusions

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