We design ScaleTraversal, an interactive tool for creating multi-scale 3D demonstration animations with limited resources for users who are unavailable to access high performance machines such as clusters or super computers. It is difficult to create 3D demonstration animations for multi-scale data. First, it is challenging to strike a balance between flexibility and user friendliness to design the user interface in customizing demonstration animations. Second, the multi-scale biomedical data is often characterized as large-size so that it is hard for users to handle it by a desktop PC. We design an interactive bi-functional user interface to create multi-scale biomedical demonstration animations intuitively. It fully utilizes the strengths of graphical interface's user friendliness and textual interface's flexibility, which enables users to customize demonstration animations from macro-scales to meso- and micro-scales. Furthermore, we design three scale-based memory management strategies to solve the issues presented in multi-scale data, including a streaming data processing strategy, a scale-based data prefetching strategy and a GPU acceleration strategy for rendering. Finally, we conduct both quantitative evaluation and qualitative evaluation to demonstrate the efficiency, expressiveness and usability of ScaleTraversal.

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

Eye movements are closely related to the cognitive processes and often act as a window to the brain and mind. To facilitate a window to the brain using eye movements, we propose EyeFIX, an interactive visual analytics interface. Our interface currently focuses on processing gaze movements to generate the two most prominent events, fixations, and saccades which are used in a large part of the eye movement literature. Our interface has multiple interactive widgets that allow the users to choose the algorithm and tune its hyper parameters that control how fixations and saccades are generated. It has four major visualizations: (1) A Gaze Plot to showcase the gaze movement of each participant, (2) a Fixation Plot that helps to identify regions of the stimulus which are of interest to a particular participant, (3) a Heat Map to study the denser regions of fixation and regions which the participant frequently visited, and (4) a Timeline Visualization to assist in having a closer look at temporal regions of interest. We design a set of brushing and linking operators that allow users to interactively control all the visualizations to dig deeper in both, the spatial and temporal domains. We demonstrate the applicability of our interface with datasets obtained by human subjects viewing naturalistic stimuli while performing various viewing tasks, including visual exploration, visual search, and prolonged visual fixation.

Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.

Eye movements recorded for many study participants are difficult to interpret, in particular when the task is to identify similar scanning strategies over space, time, and participants. In this paper we describe an approach in which we first compare scanpaths, not only based on Jaccard (JD) and bounding box (BB) similarities, but also on more complex approaches like longest common subsequence (LCS), Frechet distance (FD), dynamic time warping (DTW), and edit distance (ED). The results of these algorithms generate a weighted comparison matrix while each entry encodes the pairwise participant scanpath comparison strength. To better identify participant groups of similar eye movement behavior we reorder this matrix by hierarchical clustering, optimal-leaf ordering, dimensionality reduction, or a spectral approach. The matrix visualization is linked to the original stimulus overplotted with visual attention maps and gaze plots on which typical interactions like temporal, spatial, or participant-based filtering can be applied.

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.

In this paper we describe an interactive and web-based visual analytics tool combining linked visualization techniques and algorithmic approaches for exploring the hierarchical visual scanning behavior of a group of people when solving tasks in a static stimulus. This has the benefit that the recorded eye movement data can be observed in a more structured way with the goal to find patterns in the common scanning behavior of a group of eye tracked people. To reach this goal we first preprocess and aggregate the scanpaths based on formerly defined areas of interest (AOIs) which generates a weighted directed graph. We visually represent the resulting AOI graph as a modified hierarchical graph layout. This can be used to filter and navigate in the eye movement data shown in a separate view overplotted on the stimulus for preserving the mental map and for providing an intuitive view on the semantics of the original stimulus. Several interaction techniques and complementary views with visualizations are implemented. Moreover, due to the web-based nature of the tool, users can upload, share, and explore data with others. To illustrate the usefulness of our concept we apply it to real-world eye movement data from a formerly conducted eye tracking experiment.

In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.