Automatic Adjustment of Transfer Functions for 3D Volume Visualization.

摘要: In most volume rendering scenarios implicit classification is performed manually by specification of a transfer function, that maps abstract data values to visual attributes. An appropriate requires both specialized knowledge the interesting structures within set as well technical knowhow computer scientist. Recent automatic data-driven techniques are verywell capable separating different regions in set. However, their applicability practice limited, since they do not contain any information about critical which interest. this scenario we propose an efficient and reproducible way automatically assign function templates, include individual personal taste. The presented approach based on dynamic programming was successfully applied medical environment.