Evaluating stereo and motion cues for visualizing information nets in three dimensions
ACM Transactions on Graphics (TOG)
Proceedings of the 7th conference on Visualization '96
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Conveying the 3D Shape of Smoothly Curving Transparent Surfaces via Texture
IEEE Transactions on Visualization and Computer Graphics
Robust estimation of multiple surface shapes from occluded textures
ISCV '95 Proceedings of the International Symposium on Computer Vision
A method for the perceptual optimization of complex visualizations
Proceedings of the Working Conference on Advanced Visual Interfaces
An Approach to the Perceptual Optimization of Complex Visualizations
IEEE Transactions on Visualization and Computer Graphics
Sketching shiny surfaces: 3D shape extraction and depiction of specular surfaces
ACM Transactions on Applied Perception (TAP)
Texturing of Layered Surfaces for Optimal Viewing
IEEE Transactions on Visualization and Computer Graphics
Grid With a View: Optimal Texturing for Perception of Layered Surface Shape
IEEE Transactions on Visualization and Computer Graphics
Effective texture models for three dimensional flow visualization
Proceedings of the 28th Spring Conference on Computer Graphics
| Hi-index | 0.00 |
In this paper, we take a new look at the problem of texturing surfaces so that they can be displayed layered over each other but remain clearly visible. Finding optimal textures that solve this problem is complex because of the perceptual interactions between the visual effects of parameters controlling texture generation. Instead of using controlled experiments to investigate this problem, we use a genetic algorithm based human-in-the-loop parameter space search to build a large database of human-rated textures. This database is then analyzed with a varity of datamining techniques, including clustering, principle component analysis, neural networks, and histogram analysis. We detail this analysis, concluding with a set of guidelines for building strong layered surface textures, and a display of a number of example textures.