Hyper-Objective Vortices.
IEEE transactions on visualization and computer graphics(2020)
摘要
Almost all properties of vector fields, including magnitude, direction,
$\lambda _2$ λ 2
and vorticity change under arbitrary movements of the observer. This is undesirable since measurements of physical properties should ideally not depend on the way the (virtual) measurement device moves. There are some properties that are invariant under certain types of reference frame transformations: Galilean invariance (invariance under equal-speed translation) and objectivity (invariance under any smooth rotation and translation of the reference frame). In this paper, we introduce even harder conditions than objectivity: we demand invariance under any smooth similarity transformation (rotation, translation and uniform scale) as well as invariance under any smooth affine transformation of the reference frame. We show that these new hyper-objective measures allow the extraction of vortices that change their volume or deform. Further, we present a generic approach that transforms almost any vortex measure into a hyper-objective one. We apply our methods to vortex extraction in 2D and 3D vector fields, and analyze the numerical robustness, extraction time and the minimization residuals for the Galilean invariant, objective, and the two new hyper-objective approaches.
更多查看译文
关键词
Tensile stress,Two dimensional displays,Three-dimensional displays,Feature extraction,Sea measurements,Strain,Transforms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络