title: Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms.
abstract: Visual exploration of volumetric datasets to discover the embedded features and spatial structures is a challenging and tedious task. In this paper we present a semi-automatic approach to this problem that works by visually segmenting the intensity-gradient 2D histogram of a volumetric dataset into an exploration hierarchy. Our approach mimics user exploration behavior by analyzing the histogram with the normalized-cut multilevel segmentation technique. Unlike previous work in this area, our technique segments the histogram into a reasonable set of intuitive components that are mutually exclusive and collectively exhaustive. We use information-theoretic measures of the volumetric data segments to guide the exploration. This provides a data-driven coarse-to-fine hierarchy for a user to interactively navigate the volume in a meaningful manner.
authors.affiliation: Institute for Advanced Computer Studies, University of Maryland, College Park, USA. ipcy@cs.umd.edu
authors.forename: Cheuk Yiu
authors.initials: CY
authors.lastname: Ip
authors.forename: A
authors.initials: A
authors.lastname: Varshney
authors.forename: J
authors.initials: J
authors.lastname: JaJa