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Improving Perception of Molecular Surface visualizations by Incorporating Translucency Effects
Full paper
Molecular surfaces are a commonly used representation in the analysis of molecular structures as they provide a compact description of the space occupied by a molecule and its accessibility. However, due to the high abstraction of the atomic data, fine grain features are hard to identify. Moreover, these representations involve a high degree of occlusions, which prevents the identification of internal features and potentially impacts shape perception. In this paper, we present a set of techniques which are inspired by the properties of translucent materials, that have been developed to improve the perception of molecular surfaces: First, we introduce an interactive algorithm to simulate subsurface scattering for molecular surfaces, in order to improve the thickness perception of the molecule. Second, we present a technique to visualize structures just beneath the surface, by still conveying relevant depth information. And lastly, we introduce reflections and refractions into our visualization that improve the shape perception of molecular surfaces. We evaluate the benefits of these methods through crowd-sourced user studies as well as the feedback from several domain experts.
Semantic Screen-Space Occlusion for Multiscale Molecular Visualization
Short paper
Visual clutter is a major problem in large biological data visualization. It is often addressed through the means of level of detail schemes coupled with an appropriate coloring of the visualized structures. Ambient occlusion and shadows are often used to improve the depth perception. However, when used excessively, these techniques are sources of visual clutter themselves. In this paper we present a new approach to screen-space illumination algorithms suitable for use in illustrative visualization. The illumination effect can be controlled so that desired levels of semantic scene organization cast shadows while other remain flat. This way the illumination design can be parameterized to keep visual clutter, originating from illumination, to a minimum, while also guiding the user in a multiscale model exploration. We achieve this by selectively applying occlusion shading based on the inherent semantics of the visualized hierarchically-organized data. The technique is in principle generally applicable to any hierarchically organized 3D scene and has been demonstrated on an exemplary scene from integrative structural biology.
Visual Exploratory Analysis for Multiple T-Maze Studies
Full paper
Evaluation of spatial learning and memory in rodents is commonly carried out using different maze settings such as the Multiple T-Maze. State-of-the-art analysis is primarily based on statistics of quantitative measures stemming from animal trajectories in a maze, e.g. path length or correct decisions made. Currently, trajectories themselves are analyzed and evaluated one at a time and comparison of multiple trajectories is a tedious task. Resulting findings do not fully answer questions to complex problems which behavioral researchers encounter, why do animals behave in a certain way or are there unusual cases. This paper describes a novel approach on how exploratory analysis for Multiple T-Maze studies can be enhanced through interactive visual analysis. We explain our solution for analyzing a whole ensemble of data at once and support finding of orientation characteristics and migration patterns within the ensemble. We also abstract the analysis tasks for Multiple T-Maze studies, and based on these tasks, we extend a coordinated multiple views system to support solving the fundamental problems which behavioral researchers face. Besides standard views, we deploy the multi-resolution heatmap and the Gate-O-Gon . The GateO-Gon is a novel visual element which gives clues on the general movement orientation and distribution of revisited gates, and enhances finding of patterns in movement and identifying of irregular behavior. We demonstrate the usefulness of the newly proposed approach using a real life data set consisting of 400 Multiple T-Maze runs.