2,202 research outputs found
Velocity estimation via registration-guided least-squares inversion
This paper introduces an iterative scheme for acoustic model inversion where
the notion of proximity of two traces is not the usual least-squares distance,
but instead involves registration as in image processing. Observed data are
matched to predicted waveforms via piecewise-polynomial warpings, obtained by
solving a nonconvex optimization problem in a multiscale fashion from low to
high frequencies. This multiscale process requires defining low-frequency
augmented signals in order to seed the frequency sweep at zero frequency.
Custom adjoint sources are then defined from the warped waveforms. The proposed
velocity updates are obtained as the migration of these adjoint sources, and
cannot be interpreted as the negative gradient of any given objective function.
The new method, referred to as RGLS, is successfully applied to a few scenarios
of model velocity estimation in the transmission setting. We show that the new
method can converge to the correct model in situations where conventional
least-squares inversion suffers from cycle-skipping and converges to a spurious
model.Comment: 20 pages, 13 figures, 1 tabl
Onboard image correction
A processor architecture for performing onboard geometric and radiometric correction of LANDSAT imagery is described. The design uses a general purpose processor to calculate the distortion values at selected points in the image and a special purpose processor to resample (calculate distortion at each image point and interpolate the intensity) the sensor output data. A distinct special purpose processor is used for each spectral band. Because of the sensor's high output data rate, 80 M bit per second, the special purpose processors use a pipeline architecture. Sizing has been done on both the general and special purpose hardware
Currents and finite elements as tools for shape space
The nonlinear spaces of shapes (unparameterized immersed curves or
submanifolds) are of interest for many applications in image analysis, such as
the identification of shapes that are similar modulo the action of some group.
In this paper we study a general representation of shapes that is based on
linear spaces and is suitable for numerical discretization, being robust to
noise. We develop the theory of currents for shape spaces by considering both
the analytic and numerical aspects of the problem. In particular, we study the
analytical properties of the current map and the norm that it induces
on shapes. We determine the conditions under which the current determines the
shape. We then provide a finite element discretization of the currents that is
a practical computational tool for shapes. Finally, we demonstrate this
approach on a variety of examples
Preserving Derivative Information while Transforming Neuronal Curves
The international neuroscience community is building the first comprehensive
atlases of brain cell types to understand how the brain functions from a higher
resolution, and more integrated perspective than ever before. In order to build
these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal
cortical neurons etc.) are traced in individual brain samples by placing points
along dendrites and axons. Then, the traces are mapped to common coordinate
systems by transforming the positions of their points, which neglects how the
transformation bends the line segments in between. In this work, we apply the
theory of jets to describe how to preserve derivatives of neuron traces up to
any order. We provide a framework to compute possible error introduced by
standard mapping methods, which involves the Jacobian of the mapping
transformation. We show how our first order method improves mapping accuracy in
both simulated and real neuron traces under random diffeomorphisms. Our method
is freely available in our open-source Python package brainlit
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