18,836 research outputs found
Likelihood-Free Parallel Tempering
Approximate Bayesian Computational (ABC) methods (or likelihood-free methods)
have appeared in the past fifteen years as useful methods to perform Bayesian
analyses when the likelihood is analytically or computationally intractable.
Several ABC methods have been proposed: Monte Carlo Markov Chains (MCMC)
methods have been developped by Marjoramet al. (2003) and by Bortotet al.
(2007) for instance, and sequential methods have been proposed among others by
Sissonet al. (2007), Beaumont et al. (2009) and Del Moral et al. (2009). Until
now, while ABC-MCMC methods remain the reference, sequential ABC methods have
appeared to outperforms them (see for example McKinley et al. (2009) or Sisson
et al. (2007)). In this paper a new algorithm combining population-based MCMC
methods with ABC requirements is proposed, using an analogy with the Parallel
Tempering algorithm (Geyer, 1991). Performances are compared with existing ABC
algorithms on simulations and on a real example
An improved rotation-invariant thinning algorithm
Ahmed & Ward have recently presented an elegant, rule-based rotation-invariant thinning algorithm to produce a single-pixel wide skeleton from a binary image. We show examples where this algorithm fails on two-pixel wide lines and propose a modified method which corrects this shortcoming based on graph connectivity
Fast and robust curve skeletonization for real-world elongated objects
We consider the problem of extracting curve skeletons of three-dimensional,
elongated objects given a noisy surface, which has applications in agricultural
contexts such as extracting the branching structure of plants. We describe an
efficient and robust method based on breadth-first search that can determine
curve skeletons in these contexts. Our approach is capable of automatically
detecting junction points as well as spurious segments and loops. All of that
is accomplished with only one user-adjustable parameter. The run time of our
method ranges from hundreds of milliseconds to less than four seconds on large,
challenging datasets, which makes it appropriate for situations where real-time
decision making is needed. Experiments on synthetic models as well as on data
from real world objects, some of which were collected in challenging field
conditions, show that our approach compares favorably to classical thinning
algorithms as well as to recent contributions to the field.Comment: 47 pages; IEEE WACV 2018, main paper and supplementary materia
Combined 3D thinning and greedy algorithm to approximate realistic particles with corrected mechanical properties
The shape of irregular particles has significant influence on micro- and
macro-scopic behavior of granular systems. This paper presents a combined 3D
thinning and greedy set-covering algorithm to approximate realistic particles
with a clump of overlapping spheres for discrete element method (DEM)
simulations. First, the particle medial surface (or surface skeleton), from
which all candidate (maximal inscribed) spheres can be generated, is computed
by the topological 3D thinning. Then, the clump generation procedure is
converted into a greedy set-covering (SCP) problem.
To correct the mass distribution due to highly overlapped spheres inside the
clump, linear programming (LP) is used to adjust the density of each component
sphere, such that the aggregate properties mass, center of mass and inertia
tensor are identical or close enough to the prototypical particle. In order to
find the optimal approximation accuracy (volume coverage: ratio of clump's
volume to the original particle's volume), particle flow of 3 different shapes
in a rotating drum are conducted. It was observed that the dynamic angle of
repose starts to converge for all particle shapes at 85% volume coverage
(spheres per clump < 30), which implies the possible optimal resolution to
capture the mechanical behavior of the system.Comment: 34 pages, 13 figure
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