Convolutional neural networks (CNNs) constructed natively on the sphere have been
developed recently and shown to be highly effective for the analysis of spherical data. While …

Scale-discretised wavelets yield a directional wavelet framework on the sphere where a
signal can be probed not only in scale and position but also in orientation. Furthermore, a …

This paper gives a survey of methods for the construction of space–frequency concentrated
frames on Riemannian manifolds with bounded curvature, and the applications of these …

Cosmic strings are a well-motivated extension to the standard cosmological model and
could induce a subdominant component in the anisotropies of the cosmic microwave …

We construct the spin flaglet transform, a wavelet transform to analyze spin signals in three
dimensions. Spin flaglets can probe signal content localized simultaneously in space and …

We construct a directional spin wavelet framework on the sphere by generalising the scalar
scale-discretised wavelet transform to signals of arbitrary spin. The resulting framework is …

This paper focuses on vector random fields defined on S^d*R^k, d≧2 and k≧1, with
covariance functions that depend on the geodesic distance in S^d and on the separation …

We develop techniques to solve ill-posed inverse problems on the sphere by sparse
regularization, exploiting sparsity in both axisymmetric and directional scale-discretized …

We revisit the spherical Radon transform, also called the Funk-Radon transform, viewing it
as an axisymmetric convolution on the sphere. Viewing the spherical Radon transform in this …

Abstract Analysis on the unit sphere S^ 2 S 2 found many applications in seismology,
weather prediction, astrophysics, signal analysis, crystallography, computer vision …