Search results
Results from the WOW.Com Content Network
The vector Laplace operator, also denoted by , is a differential operator defined over a vector field. [7] The vector Laplacian is similar to the scalar Laplacian; whereas the scalar Laplacian applies to a scalar field and returns a scalar quantity, the vector Laplacian applies to a vector field , returning a vector quantity.
The Laplacian vector field theory is being used in research in mathematics and medicine. Math researchers study the boundary values for Laplacian vector fields and investigate an innovative approach where they assume the surface is fractal and then must utilize methods for calculating a well-defined integration over the boundary. [5]
The connection Laplacian, also known as the rough Laplacian, is a differential operator acting on the various tensor bundles of a manifold, defined in terms of a Riemannian- or pseudo-Riemannian metric. When applied to functions (i.e. tensors of rank 0), the connection Laplacian is often called the Laplace–Beltrami operator.
For any twice-differentiable real-valued function f defined on Euclidean space R n, the Laplace operator (also known as the Laplacian) takes f to the divergence of its gradient vector field, which is the sum of the n pure second derivatives of f with respect to each vector of an orthonormal basis for R n.
For a tensor field of order k > 1, the tensor field of order k is defined by the recursive relation = where is an arbitrary constant vector. A tensor field of order greater than one may be decomposed into a sum of outer products, and then the following identity may be used: = ().
Or, for different anisotropic effects using the same vector field [14] θ = arctan ( V y / − V x ) {\displaystyle \theta =\arctan(V_{y}/-V_{x})} It is important to note that, regardless of the values of θ {\displaystyle \theta } , the anisotropic propagation will occur parallel to the secondary direction c2 and perpendicular to the ...
In some applications, the sampling of the data is generally not related to the geometry of the manifold we are interested in describing. In this case, we can set = and the diffusion operator approximates the Laplace–Beltrami operator. We then recover the Riemannian geometry of the data set regardless of the distribution of the points.
In (Lindeberg 2013b, 2015) [2] [3] it is shown that the determinant of the Hessian operator performs significantly better than the Laplacian operator or its difference-of-Gaussians approximation, as well as better than the Harris or Harris-Laplace operators, for image-based matching using local SIFT-like or SURF-like image descriptors, leading ...