Salient Representation of Volume Data
We introduce a novel method for identification of objects of interest in volume data. Our approach conveys the information contained in two essentially different concepts, the object’s boundaries and the narrow solid structures, in an easy and uniform way. The second order derivative operators in directions reaching minimal response are employed for this task. To show the superior performance of our method, we provide a comparison with its main competitor—surface extraction from areas of maximal gradient magnitude. We show that our approach provides the possibility to represent volume data by a subset of a nominal size.
KeywordsHessian Matrix Salience Function Gradient Magnitude Volume Visualization Direct Volume Rendering
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