3,590 research outputs found
Three-dimensional measurements with a novel technique combination of confocal and focus variation with a simultaneous scan
The most common optical measurement technologies used today for the three dimensional measurement of technical surfaces are Coherence Scanning Interferometry (CSI), Imaging Confocal Microscopy (IC), and Focus Variation (FV). Each one has its benefits and its drawbacks. FV will be the ideal technology for the measurement of those regions where the slopes are high and where the surface is very rough, while CSI and IC will provide better results for smoother and flatter surface regions. In this work we investigated the benefits and drawbacks of combining Interferometry, Confocal and focus variation to get better measurement of technical surfaces. We investigated a way of using Microdisplay Scanning type of Confocal Microscope to acquire on a simultaneous scan confocal and focus Variation information to reconstruct a three dimensional measurement. Several methods are presented to fuse the optical sectioning properties of both techniques as well as the topographical information. This work shows the benefit of this combination technique on several industrial samples where neither confocal nor focus variation is able to provide optimal results.Postprint (author's final draft
Consistent ICP for the registration of sparse and inhomogeneous point clouds
In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
An eccentric companion at the edge of the brown dwarf desert orbiting the 2.4 Msun giant star HIP67537
We report the discovery of a substellar companion around the giant star
HIP67537. Based on precision radial velocity measurements from CHIRON and FEROS
high-resolution spectroscopic data, we derived the following orbital elements
for HIP67537: msin = 11.1 M,
= 4.9 AU and = 0.59. Considering
random inclination angles, this object has 65% probability to be
above the theoretical deuterium-burning limit, thus it is one of the few known
objects in the planet to brown-dwarf transition region. In addition, we
analyzed the Hipparcos astrometric data of this star, from which we derived a
minimum inclination angle for the companion of 2 deg. This value
corresponds to an upper mass limit of 0.3 M, therefore the
probability that HIP67537 is stellar in nature is 7%. The large
mass of the host star and the high orbital eccentricity makes HIP67537 a
very interesting and rare substellar object. This is the second candidate
companion in the brown dwarf desert detected in the sample of intermediate-mass
stars targeted by the EXPRESS radial velocity program, which corresponds to a
detection fraction of = 1.6%. This value is larger than the
fraction observed in solar-type stars, providing new observational evidence of
an enhanced formation efficiency of massive substellar companions in massive
disks. Finally, we speculate about different formation channels for this
object.Comment: Accepted for publication to A&
Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI
Purpose: To develop a MRI acquisition and reconstruction framework for
volumetric cine visualisation of the fetal heart and great vessels in the
presence of maternal and fetal motion.
Methods: Four-dimensional depiction was achieved using a highly-accelerated
multi-planar real-time balanced steady state free precession acquisition
combined with retrospective image-domain techniques for motion correction,
cardiac synchronisation and outlier rejection. The framework was evaluated and
optimised using a numerical phantom, and evaluated in a study of 20 mid- to
late-gestational age human fetal subjects. Reconstructed cine volumes were
evaluated by experienced cardiologists and compared with matched ultrasound. A
preliminary assessment of flow-sensitive reconstruction using the velocity
information encoded in the phase of dynamic images is included.
Results: Reconstructed cine volumes could be visualised in any 2D plane
without the need for highly-specific scan plane prescription prior to
acquisition or for maternal breath hold to minimise motion. Reconstruction was
fully automated aside from user-specified masks of the fetal heart and chest.
The framework proved robust when applied to fetal data and simulations
confirmed that spatial and temporal features could be reliably recovered.
Expert evaluation suggested the reconstructed volumes can be used for
comprehensive assessment of the fetal heart, either as an adjunct to ultrasound
or in combination with other MRI techniques.
Conclusion: The proposed methods show promise as a framework for
motion-compensated 4D assessment of the fetal heart and great vessels
Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation
PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS: Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION: This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI
Studying the photometric and spectroscopic variability of the magnetic hot supergiant Orionis Aa
Massive stars play a significant role in the chemical and dynamical evolution
of galaxies. However, much of their variability, particularly during their
evolved supergiant stage, is poorly understood. To understand the variability
of evolved massive stars in more detail, we present a study of the O9.2Ib
supergiant Ori Aa, the only currently confirmed supergiant to host a
magnetic field. We have obtained two-color space-based BRIght Target Explorer
photometry (BRITE) for Ori Aa during two observing campaigns, as well
as simultaneous ground-based, high-resolution optical CHIRON spectroscopy. We
perform a detailed frequency analysis to detect and characterize the star's
periodic variability. We detect two significant, independent frequencies, their
higher harmonics, and combination frequencies: the stellar rotation period
d, most likely related to the presence of the
stable magnetic poles, and a variation with a period of d
attributed to circumstellar environment, also detected in the H and
several He I lines, yet absent in the purely photospheric lines. We confirm the
variability with /4, likely caused by surface
inhomogeneities, being the possible photospheric drivers of the discrete
absorption components. No stellar pulsations were detected in the data. The
level of circumstellar activity clearly differs between the two BRITE observing
campaigns. We demonstrate that Ori Aa is a highly variable star with
both periodic and non-periodic variations, as well as episodic events. The
rotation period we determined agrees well with the spectropolarimetric value
from the literature. The changing activity level observed with BRITE could
explain why the rotational modulation of the magnetic measurements was not
clearly detected at all epochs.Comment: 20 pages, 5 tables, 12 figures, accepted for publication in A&
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been
presented in last decade using different sensor modalities. However, robust
SLAM in extreme weather conditions is still an open research problem. In this
paper, RadarSLAM, a full radar based graph SLAM system, is proposed for
reliable localization and mapping in large-scale environments. It is composed
of pose tracking, local mapping, loop closure detection and pose graph
optimization, enhanced by novel feature matching and probabilistic point cloud
generation on radar images. Extensive experiments are conducted on a public
radar dataset and several self-collected radar sequences, demonstrating the
state-of-the-art reliability and localization accuracy in various adverse
weather conditions, such as dark night, dense fog and heavy snowfall
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