74,002 research outputs found
Reconstructing polygons from moments with connections to array processing
Caption title.Includes bibliographical references (p. 24-26).Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the US Army Research Office. DAAL03-92-G-0115 Supported by the National Science Foundation. MIP-9015281 Supported by the Clement Vaturi Fellowship in Biomedical Imaging Sciences at MIT.Peyman Milanfar ... [et al.]
Moment inversion problem for piecewise D-finite functions
We consider the problem of exact reconstruction of univariate functions with
jump discontinuities at unknown positions from their moments. These functions
are assumed to satisfy an a priori unknown linear homogeneous differential
equation with polynomial coefficients on each continuity interval. Therefore,
they may be specified by a finite amount of information. This reconstruction
problem has practical importance in Signal Processing and other applications.
It is somewhat of a ``folklore'' that the sequence of the moments of such
``piecewise D-finite''functions satisfies a linear recurrence relation of
bounded order and degree. We derive this recurrence relation explicitly. It
turns out that the coefficients of the differential operator which annihilates
every piece of the function, as well as the locations of the discontinuities,
appear in this recurrence in a precisely controlled manner. This leads to the
formulation of a generic algorithm for reconstructing a piecewise D-finite
function from its moments. We investigate the conditions for solvability of the
resulting linear systems in the general case, as well as analyze a few
particular examples. We provide results of numerical simulations for several
types of signals, which test the sensitivity of the proposed algorithm to
noise
Sampling and Reconstruction of Shapes with Algebraic Boundaries
We present a sampling theory for a class of binary images with finite rate of
innovation (FRI). Every image in our model is the restriction of
\mathds{1}_{\{p\leq0\}} to the image plane, where \mathds{1} denotes the
indicator function and is some real bivariate polynomial. This particularly
means that the boundaries in the image form a subset of an algebraic curve with
the implicit polynomial . We show that the image parameters --i.e., the
polynomial coefficients-- satisfy a set of linear annihilation equations with
the coefficients being the image moments. The inherent sensitivity of the
moments to noise makes the reconstruction process numerically unstable and
narrows the choice of the sampling kernels to polynomial reproducing kernels.
As a remedy to these problems, we replace conventional moments with more stable
\emph{generalized moments} that are adjusted to the given sampling kernel. The
benefits are threefold: (1) it relaxes the requirements on the sampling
kernels, (2) produces annihilation equations that are robust at numerical
precision, and (3) extends the results to images with unbounded boundaries. We
further reduce the sensitivity of the reconstruction process to noise by taking
into account the sign of the polynomial at certain points, and sequentially
enforcing measurement consistency. We consider various numerical experiments to
demonstrate the performance of our algorithm in reconstructing binary images,
including low to moderate noise levels and a range of realistic sampling
kernels.Comment: 12 pages, 14 figure
Reconstructing parton distribution function based on maximum entropy method
A new method based on the maximum entropy principle for reconstructing the
parton distribution function (PDF) from moments is proposed. Unlike traditional
methods, the new method no longer needs to introduce any artificial
assumptions. For the case of moments with errors, we introduce Gaussian
functions to soften the constraints of moments. A series of tests are conducted
to comprehensively evaluate the validity and reconstruction efficiency of this
new method. And these tests indicate that our method is reasonable and can
achieve high-quality reconstruction with at least the first six moments as
input. Finally, we select a set of lattice QCD results regarding moments as
input and provide reasonable reconstruction results.Comment: 6 pages, 8 figure
Maximum Entropy Reconstruction Of Moment Coded Images
The maximum entropy principle (MEP) is applied to the problem of reconstructing an image from knowledge of a finite set of its moments. This new approach is compared to the existing method of moments approach and is shown to have a clear edge in performance in all of the applications attempted. Compression ratios more than twice as high as those previously achieved are possible with the new MEP method
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