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The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D(More)
The fundamental goal of the GeoVISTA Studio project is to improve geoscientific analysis by providing an environment that operationally integrates a wide range of analysis activities, including those both computationally and visually based. We argue here that improving the infrastructure used in analysis has far-reaching potential to better integrate(More)
Large displays are everywhere. However, the computer mouse remains the most common interaction tool for such displays. We propose a new approach for fingertip interaction with large display systems using monocular computer vision. By taking into account the location of the user and the interaction area available, we can estimate an interaction surface -(More)
Computer technologies have been rapidly improving throughout the last couple of decades, and they are now at the stage of allowing scientists to carry out data analyses that deal with very complex and multivariate datasets. Moreover, there are growing numbers of researchers who wish to carry out such tasks in real-time. Traditional data analyses and(More)
Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the " border effect ". In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce a(More)
Transfer functions facilitate the volumetric data visualization by assigning optical properties to various data features and scalar values. Automation of transfer function specifications still remains a challenge in volume rendering. This paper presents an approach for automating transfer function generations by utilizing topological attributes derived from(More)