526,380 research outputs found
3D statistical facial reconstruction
The aim of craniofacial reconstruction is to produce a likeness of a face
from the skull. Few works in computerized assisted facial reconstruction have
been done in the past, due to poor machine performances and data availability,
and major works are manually reconstructions. In this paper, we present an
approach to build 3D statistical models of the skull and the face with soft
tissues from the skull of one individual. Results on real data are presented
and seem promising
Statistical Reconstruction of Qutrits
We discuss a procedure of measurement followed by the reproduction of the
quantum state of a three-level optical system - a frequency- and spatially
degenerate two-photon field. The method of statistical estimation of the
quantum state based on solving the likelihood equation and analyzing the
statistical properties of the obtained estimates is developed. Using the root
approach of estimating quantum states, the initial two-photon state vector is
reproduced from the measured fourth moments in the field . The developed
approach applied to quantum states reconstruction is based on the amplitudes of
mutually complementary processes. Classical algorithm of statistical estimation
based on the Fisher information matrix is generalized to the case of quantum
systems obeying Bohr's complementarity principle. It has been experimentally
proved that biphoton-qutrit states can be reconstructed with the fidelity of
0.995-0.999 and higher.Comment: Submitted to Physical Review
Bayesian Reconstruction of Missing Observations
We focus on an interpolation method referred to Bayesian reconstruction in
this paper. Whereas in standard interpolation methods missing data are
interpolated deterministically, in Bayesian reconstruction, missing data are
interpolated probabilistically using a Bayesian treatment. In this paper, we
address the framework of Bayesian reconstruction and its application to the
traffic data reconstruction problem in the field of traffic engineering. In the
latter part of this paper, we describe the evaluation of the statistical
performance of our Bayesian traffic reconstruction model using a statistical
mechanical approach and clarify its statistical behavior
Accurate object reconstruction by statistical moments
Statistical moments can offer a powerful means for object description in object sequences. Moments used in this way provide a description of the changing shape of the object with time. Using these descriptions to predict temporal views of the object requires efficient and accurate reconstruction of the object from a limited set of moments, but accurate reconstruction from moments has as yet received only limited attention. We show how we can improve accuracy not only by consideration of formulation, but also by a new adaptive thresholding technique that removes one parameter needed in reconstruction. Both approaches are equally applicable for Legendre and other orthogonal moments to improve accuracy in reconstruction
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Statistical physics-based reconstruction in compressed sensing
Compressed sensing is triggering a major evolution in signal acquisition. It
consists in sampling a sparse signal at low rate and later using computational
power for its exact reconstruction, so that only the necessary information is
measured. Currently used reconstruction techniques are, however, limited to
acquisition rates larger than the true density of the signal. We design a new
procedure which is able to reconstruct exactly the signal with a number of
measurements that approaches the theoretical limit in the limit of large
systems. It is based on the joint use of three essential ingredients: a
probabilistic approach to signal reconstruction, a message-passing algorithm
adapted from belief propagation, and a careful design of the measurement matrix
inspired from the theory of crystal nucleation. The performance of this new
algorithm is analyzed by statistical physics methods. The obtained improvement
is confirmed by numerical studies of several cases.Comment: 20 pages, 8 figures, 3 tables. Related codes and data are available
at http://aspics.krzakala.or
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