An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Maximum entropy light source placement
Proceedings of the conference on Visualization '02
Viewpoint Selection using Viewpoint Entropy
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Importance-Driven Focus of Attention
IEEE Transactions on Visualization and Computer Graphics
Dynamic View Selection for Time-Varying Volumes
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Information Theory
The Normalized Compression Distance Is Resistant to Noise
IEEE Transactions on Information Theory
Viewpoint selection for intervention planning
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
An Adaptive Cutaway with Volume Context Preservation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Automatic upright orientation and good view recognition for 3D man-made models
Pattern Recognition
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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.