929 research outputs found
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
The Ellipsoid Factor for quantification of rods, plates and intermediate forms in 3D geometries
The Ellipsoid Factor (EF) is a method for the local determination of the rod- or plate-like nature of porous or spongy continua. EF at a point within a 3D structure is defined as the difference in axis ratios of the greatest ellipsoid which fits inside the structure and which contains the point of interest, and ranges from -1 for strongly oblate (discus-shaped) ellipsoids, to +1 for strongly prolate (javelin-shaped) ellipsoids. For an ellipsoid with axes a ≤ b ≤ c, EF = a/b – b/c. Here, EF is demonstrated in a Java plugin, Ellipsoid Factor for ImageJ, distributed in the BoneJ plugin collection. Ellipsoid Factor utilises an ellipsoid optimisation algorithm which assumes that maximal ellipsoids are centred on the medial axis, then dilates, rotates and translates slightly each ellipsoid until it cannot increase in volume any further. Ellipsoid Factor successfully identifies rods, plates and intermediate structures within trabecular bone, and summarises the distribution of geometries with an overall EF mean and standard deviation, EF histogram and Flinn diagram displaying a/b versus b/c. Ellipsoid Factor is released to the community for testing, use, and improvement
Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition
Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated
research efforts to unveil the extremely complex micro-scale processes that
control the activity of soil microorganisms. Voxel-based description (up to
hundreds millions voxels) of the pore space can be extracted, from grey level
3D CT scanner images, by means of simple image processing tools. Classical
methods for numerical simulation of biological dynamics using mesh of voxels,
such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the
use of more compact and reliable geometrical representations of pore space can
drastically decrease the computational cost of the simulations. Several recent
works propose basic analytic volume primitives (e.g. spheres, generalized
cylinders, ellipsoids) to define a piece-wise approximation of pore space for
numerical simulation of draining, diffusion and microbial decomposition. Such
approaches work well but the drawback is that it generates approximation
errors. In the present work, we study another alternative where pore space is
described by means of geometrically relevant connected subsets of voxels
(regions) computed from the curvilinear skeleton. Indeed, many works use the
curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes
within various domains (medicine, material sciences, petroleum engineering,
etc.) but only a few ones in soil sciences. Within the context of soil
sciences, most studies dealing with 3D medial axis focus on the determination
of pore throats. Here, we segment pore space using curvilinear skeleton in
order to achieve numerical simulation of microbial decomposition (including
diffusion processes). We validate simulation outputs by comparison with other
methods using different pore space geometrical representations (balls, voxels).Comment: preprint, submitted to Computers & Geosciences 202
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