327 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
Nano1D: An accurate Computer Vision model for segmentation and analysis of low-dimensional objects
Microscopy images are usually analyzed qualitatively or manually and there is
a need for autonomous quantitative analysis of objects. In this paper, we
present a physics-based computational model for accurate segmentation and
geometrical analysis of one-dimensional irregular and deformable objects from
microscopy images. This model, named Nano1D, has four steps of preprocessing,
segmentation, separating overlapped objects and geometrical measurements. The
model is tested on Ag nanowires, and successfully segments and analyzes their
geometrical characteristics including length, width and distributions. The
function of the algorithm is not undermined by the size, number, density,
orientation and overlapping of objects in images. The main strength of the
model is shown to be its ability to segment and analyze overlapping objects
successfully with more than 99% accuracy, while current machine learning and
computational models suffer from inaccuracy and inability to segment
overlapping objects. Nano1D can analyze one-dimensional (1D) nanoparticles
including nanowires, nanotubes, nanorods in addition to other 1D features of
microstructures like microcracks, dislocations etc
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