3,093 research outputs found
An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration
We propose numerical algorithms for solving large deformation diffeomorphic
image registration problems. We formulate the nonrigid image registration
problem as a problem of optimal control. This leads to an infinite-dimensional
partial differential equation (PDE) constrained optimization problem.
The PDE constraint consists, in its simplest form, of a hyperbolic transport
equation for the evolution of the image intensity. The control variable is the
velocity field. Tikhonov regularization on the control ensures well-posedness.
We consider standard smoothness regularization based on - or
-seminorms. We augment this regularization scheme with a constraint on the
divergence of the velocity field rendering the deformation incompressible and
thus ensuring that the determinant of the deformation gradient is equal to one,
up to the numerical error.
We use a Fourier pseudospectral discretization in space and a Chebyshev
pseudospectral discretization in time. We use a preconditioned, globalized,
matrix-free, inexact Newton-Krylov method for numerical optimization. A
parameter continuation is designed to estimate an optimal regularization
parameter. Regularity is ensured by controlling the geometric properties of the
deformation field. Overall, we arrive at a black-box solver. We study spectral
properties of the Hessian, grid convergence, numerical accuracy, computational
efficiency, and deformation regularity of our scheme. We compare the designed
Newton-Krylov methods with a globalized preconditioned gradient descent. We
study the influence of a varying number of unknowns in time.
The reported results demonstrate excellent numerical accuracy, guaranteed
local deformation regularity, and computational efficiency with an optional
control on local mass conservation. The Newton-Krylov methods clearly
outperform the Picard method if high accuracy of the inversion is required.Comment: 32 pages; 10 figures; 9 table
Automatic Face Recognition System Based on Local Fourier-Bessel Features
We present an automatic face verification system inspired by known properties
of biological systems. In the proposed algorithm the whole image is converted
from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT).
Using the whole image is compared to the case where only face image regions
(local analysis) are considered. The resulting representations are embedded in
a dissimilarity space, where each image is represented by its distance to all
the other images, and a Pseudo-Fisher discriminator is built. Verification test
results on the FERET database showed that the local-based algorithm outperforms
the global-FBT version. The local-FBT algorithm performed as state-of-the-art
methods under different testing conditions, indicating that the proposed system
is highly robust for expression, age, and illumination variations. We also
evaluated the performance of the proposed system under strong occlusion
conditions and found that it is highly robust for up to 50% of face occlusion.
Finally, we automated completely the verification system by implementing face
and eye detection algorithms. Under this condition, the local approach was only
slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing,
18 (SIBGRAPI
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