9,654 research outputs found
Fast, asymptotically efficient, recursive estimation in a Riemannian manifold
Stochastic optimisation in Riemannian manifolds, especially the Riemannian
stochastic gradient method, has attracted much recent attention. The present
work applies stochastic optimisation to the task of recursive estimation of a
statistical parameter which belongs to a Riemannian manifold. Roughly, this
task amounts to stochastic minimisation of a statistical divergence function.
The following problem is considered : how to obtain fast, asymptotically
efficient, recursive estimates, using a Riemannian stochastic optimisation
algorithm with decreasing step sizes? In solving this problem, several original
results are introduced. First, without any convexity assumptions on the
divergence function, it is proved that, with an adequate choice of step sizes,
the algorithm computes recursive estimates which achieve a fast non-asymptotic
rate of convergence. Second, the asymptotic normality of these recursive
estimates is proved, by employing a novel linearisation technique. Third, it is
proved that, when the Fisher information metric is used to guide the algorithm,
these recursive estimates achieve an optimal asymptotic rate of convergence, in
the sense that they become asymptotically efficient. These results, while
relatively familiar in the Euclidean context, are here formulated and proved
for the first time, in the Riemannian context. In addition, they are
illustrated with a numerical application to the recursive estimation of
elliptically contoured distributions.Comment: updated version of draft submitted for publication, currently under
revie
Fast adaptive elliptical filtering using box splines
We demonstrate that it is possible to filter an image with an elliptic window
of varying size, elongation and orientation with a fixed computational cost per
pixel. Our method involves the application of a suitable global pre-integrator
followed by a pointwise-adaptive localization mesh. We present the basic theory
for the 1D case using a B-spline formalism and then appropriately extend it to
2D using radially-uniform box splines. The size and ellipticity of these
radially-uniform box splines is adaptively controlled. Moreover, they converge
to Gaussians as the order increases. Finally, we present a fast and practical
directional filtering algorithm that has the capability of adapting to the
local image features.Comment: 9 pages, 1 figur
Fast space-variant elliptical filtering using box splines
The efficient realization of linear space-variant (non-convolution) filters
is a challenging computational problem in image processing. In this paper, we
demonstrate that it is possible to filter an image with a Gaussian-like
elliptic window of varying size, elongation and orientation using a fixed
number of computations per pixel. The associated algorithm, which is based on a
family of smooth compactly supported piecewise polynomials, the
radially-uniform box splines, is realized using pre-integration and local
finite-differences. The radially-uniform box splines are constructed through
the repeated convolution of a fixed number of box distributions, which have
been suitably scaled and distributed radially in an uniform fashion. The
attractive features of these box splines are their asymptotic behavior, their
simple covariance structure, and their quasi-separability. They converge to
Gaussians with the increase of their order, and are used to approximate
anisotropic Gaussians of varying covariance simply by controlling the scales of
the constituent box distributions. Based on the second feature, we develop a
technique for continuously controlling the size, elongation and orientation of
these Gaussian-like functions. Finally, the quasi-separable structure, along
with a certain scaling property of box distributions, is used to efficiently
realize the associated space-variant elliptical filtering, which requires O(1)
computations per pixel irrespective of the shape and size of the filter.Comment: 12 figures; IEEE Transactions on Image Processing, vol. 19, 201
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