Tensor Glyph Warping – Visualizing Metric Tensor Fields using Riemannian Exponential Maps
نویسندگان
چکیده
The Riemannian exponential map, and its inverse the Riemannian logarithm map, can be used to visualize metric tensor fields. In this chapter we first derive the well-known metric sphere glyph from the geodesic equations, where the tensor field to be visualized is regarded as the metric of a manifold. These glyphs capture the appearance of the tensors relative to the coordinate system of the human observer. We then introduce two new concepts for metric tensor field visualization: geodesic spheres and geodesically warped glyphs. These additions make it possible not only to visualize tensor anisotropy, but also the curvature and change in tensorshape in a local neighborhood. The framework is based on the expp(v ) and logp(q) maps, which can be computed by solving a second order Ordinary Differential Equation (ODE) or by manipulating the geodesic distance function. The latter can be found by solving the eikonal equation, a non-linear Partial Differential Equation (PDE), or it can be derived analytically for some manifolds. To avoid heavy calculations, we also include first and second order Taylor approximations to exp and log. In our experiments, these are shown to be sufficiently accurate to produce glyphs that visually characterize anisotropy, curvature and shape-derivatives in smooth tensor fields.
منابع مشابه
Visualization of Two-Dimensional Symmetric Positive Definite Tensor Fields Using the Heat Kernel Signature
We propose a method for visualizing two-dimensional symmetric positive definite tensor fields using the Heat Kernel Signature (HKS). The HKS is derived from the heat kernel and was originally introduced as an isometry invariant shape signature. Each positive definite tensor field defines a Riemannian manifold by considering the tensor field as a Riemannian metric. On this Riemmanian manifold we...
متن کاملStrategies for Visualizing Tensor Fields in more than two Dimensions
Tensor field visualization aims at the depiction of the full information contained in the underlying data set or the extraction and display of specific features. Here, we focus on the first task and evaluate various methods with regard to their power of providing an intuitive visual representation. Tensor fields are reviewed in a differential geometric context and we provide a coordinate-free d...
متن کاملGroupwise Registration and Atlas Construction of 4th-Order Tensor Fields Using the R + Riemannian Metric
Registration of Diffusion-Weighted MR Images (DW-MRI) can be achieved by registering the corresponding 2nd-order Diffusion Tensor Images (DTI). However, it has been shown that higher-order diffusion tensors (e.g. order-4) outperform the traditional DTI in approximating complex fiber structures such as fiber crossings. In this paper we present a novel method for unbiased group-wise non-rigid reg...
متن کاملJacobi Equations and Comparison Theorems for Corank 1 Sub-riemannian Structures with Symmetries
The Jacobi curve of an extremal of optimal control problem is a curve in a Lagrangian Grassmannian defined up to a symplectic transformation and containing all information about the solutions of the Jacobi equations along this extremal. In our previous works we constructed the canonical bundle of moving frames and the complete system of symplectic invariants, called curvature maps, for parametr...
متن کاملMath 144 Notes: Riemannian Geometry
1. Manifolds: 1/7/14 1 2. Tangent and Cotangent Spaces: 1/9/14 3 3. Vector Fields, One-Forms, and Riemannian Metrics: 1/14/14 6 4. The Lie Bracket and Riemannian Connections: 1/16/14 8 5. Existence and Uniqueness of the Riemannian Connection: 1/21/14 10 6. Tensor Fields, Parallel Transport, and Holonomy: 1/23/14 13 7. The Riemann Curvature Tensor: 1/28/14 15 8. Flatness: 1/30/14 17 9. Symmetrie...
متن کامل