Terascale simulations produce data that is vast in spatial, emporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data reduction method offers a viable solution to this large data problem. This paper presents a new approach to the problem of extracting and visualizing 4D features within large volume data. Conventional methods requires either an analytical description of the feature of interest or tedious manual intervention throughout the feature extraction and tracking process ...

Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured ...

In volume data visualization, the classification step is used to determine voxel visibility and is usually carried out through the interactive editing of a transfer function that defines a mapping between voxel value and color/opacity. This approach is limited by the difficulties in working effectively in the transfer function space beyond two dimensions. We present a new approach to the volume classification problem which couples machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner ...

In volume visualization, users typically specify transfer functions to classify the data and assign visual attributes to each material class. Higher-dimensional classication makes it easier to differentiate material classes since more data properties are considered. One of the difculties in using higher-dimensional classication is the absence of appropriate user interfaces. We introduce an intuitive user interface that allows the user to work in the cluster space, which shows the material classes with a set of material widgets, rather than work in the transfer function space ...