Snap-together visualization: can users construct and operate coordinated visualizations?
International Journal of Human-Computer Studies - Empirical evaluation of information visualizations
Procedural annotation of uncertain information
Proceedings of the conference on Visualization '00
Glyphs for Visualizing Uncertainty in Vector Fields
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
User Studies: Why, How, and When?
IEEE Computer Graphics and Applications
Human Factors in Visualization Research
IEEE Transactions on Visualization and Computer Graphics
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Comparing 2D Vector Field Visualization Methods: A User Study
IEEE Transactions on Visualization and Computer Graphics
Fundamentals of Computer Graphics, Second Ed.
Fundamentals of Computer Graphics, Second Ed.
Toward Measuring Visualization Insight
IEEE Computer Graphics and Applications
Economic and subjective measures of the perceived value of aesthetics and usability
ACM Transactions on Computer-Human Interaction (TOCHI)
MATLAB Advanced GUI Development
MATLAB Advanced GUI Development
Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation
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
Pointillist and glyph-based visualization of nanoparticles in formation
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
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As collections of 2D/3D images continue to grow, interest in effective ways to visualize and explore the statistical morphological properties of a group of images has surged. Recently, deformation models have emerged as simple methods to capture the variability and statistical properties of a collection of images. Such models have proven to be effective in tasks such as image classification, generation, registration, segmentation, and analysis of modes of variation. A crucial element missing from most statistical models has been an effective way to summarize and visualize the statistical morphological properties of a group of images. This paper evaluates different visualization techniques that can be extended and used to illustrate the information captured by such statistical models. First, four illustration techniques are described as methods to summarize the statistical morphological properties as captured by deformation models. Second, results of a user study conducted to compare the effectiveness of each visualization technique are presented. After comparing the performance of 40 subjects, we found that statistical annotation techniques present significant benefits when analyzing the structural properties of a group of images.