Representative Views and Paths for Volume Models

Volume data models are becoming larger and larger as the capture technology improves. Thus, their visualization requires high computational power. The automatic presentation of volume models through representative images and/or exploration paths becomes more and more useful. Representative views are also useful for document illustration, fast data quality evaluation, or model libraries documentation. Exploration paths are also useful for video demonstrations and previsualization of captured data. In this paper we present a fast, adaptive method for the selection of representative views and the automatic generation of exploration paths for volume models. Our algorithm is based on multi-scale entropy and algorithmic complexity. These views and paths reveal informative parts of a model given a certain transfer function. We show that our method is simple and easy to incorporate in medical visualization tools.