A Cluster-Space Visual Interface for Arbitrary Dimensional Classification of Volume Data

Abstract

In volume visualization, users typically specify transfer functions to classify the data and assign visual attributes to each material class. Higher-dimensional classification makes it easier to differentiate material classes since more data properties are considered. One of the difficulties in using higher-dimensional classification 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. This interface not only provides the user the capability to specify arbitrary-dimensional transfer functions, but also allows the user to operate directly on the classification and visualization results.