Abstract
The interval volume measure is defined as the volume of the space occupied by a range of isosurfaces corresponding to an interval of isovalues. The interval volume measures are very useful since they can be taken as an alternative way of producing a smooth noise-suppressing histogram. This paper proposes two new methods (i.e., the subdividing method and the slicing method) that can calculate interval volume measures with very high accuracy for scalar regular-grid volumetric datasets. It is assumed that the underlying function inside the grid cell is defined by trilinear interpolation. A refined histogram method that can produce accurate interval volume measures is also presented in the paper. All three methods are compared against one another in terms of accuracy and performance. Their improvement for computing global and local histograms is demonstrated by comparing against the previous methods.
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Wang, C. (2019). Accurate Computation of Interval Volume Measures for Improving Histograms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_24
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DOI: https://doi.org/10.1007/978-3-030-33720-9_24
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