A taxonomy for texture description and identification
A taxonomy for texture description and identification
Spot noise texture synthesis for data visualization
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Orderable dimensions of visual texture for data display: orientation, size and contrast
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Identifying high level features of texture perception
CVGIP: Graphical Models and Image Processing
Classifying visual knowledge representations: a foundation for visualization research
VIS '90 Proceedings of the 1st conference on Visualization '90
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building perceptual textures to visualize multidimensional datasets
Proceedings of the conference on Visualization '98
Large Datasets at a Glance: Combining Textures and Colors in Scientific Visualization
IEEE Transactions on Visualization and Computer Graphics
Perceptually based brush strokes for nonphotorealistic visualization
ACM Transactions on Graphics (TOG)
Compositing color with texture for multi-variate visualization
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
A perceptually based spectral model for isotropic textures
ACM Transactions on Applied Perception (TAP)
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
An evolutionary system for near-regular texture synthesis
Pattern Recognition
Visualizing flow data using assorted glyphs
Crossroads
Ensemble-space visualization improves perception of 3D state of molecular dynamics simulation
Proceedings of the 5th symposium on Applied perception in graphics and visualization
Perceptual texture space improves perceptual consistency of computational features
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Grain size measurement of crystalline products using maximum difference method
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Perceptually-motivated graphics, visualization and 3D displays
ACM SIGGRAPH 2010 Courses
LikeMiner: a system for mining the power of 'like' in social media networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Using an oriented PDE to repair image textures
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
On the limits of resolution and visual angle in visualization
ACM Transactions on Applied Perception (TAP)
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Recently, researchers have started using texture for data visualization. The rationale behind this is to exploit the sensitivity of the human visual system to texture in order to overcome the limitations inherent in the display of multidimensional data.A fundamental issue that must be addressed is what textural features are important in texture perception, and how they are used. We designed an experiment to help identify the relevant higher order features of texture perceived by humans. We used twenty subjects, who were asked to rate 56 pictures from Brodatz's album [1] on 12 nine-point Likert scales. We applied the techniques of hierarchical cluster analysis, non-parametric multidimensional scaling (MDS), Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis to data gathered from the subjects.Based on these techniques, we identified three orthogonal dimensions for texture to be repetitive vs. non-repetitive; high-contrast and non-directional vs. low-contrast and directional; granular, coarse and low-complexity vs. non-granular, fine and high-complexity.