17,662 research outputs found
Precision pointing compensation for DSN antennas with optical distance measuring sensors
The pointing control loops of Deep Space Network (DSN) antennas do not account for unmodeled deflections of the primary and secondary reflectors. As a result, structural distortions due to unpredictable environmental loads can result in uncompensated boresight shifts which degrade pointing accuracy. The design proposed here can provide real-time bias commands to the pointing control system to compensate for environmental effects on pointing performance. The bias commands can be computed in real time from optically measured deflections at a number of points on the primary and secondary reflectors. Computer simulations with a reduced-order finite-element model of a DSN antenna validate the concept and lead to a proposed design by which a ten-to-one reduction in pointing uncertainty can be achieved under nominal uncertainty conditions
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4.Comment: This paper has been accepted by IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR 2019
eleanor: An open-source tool for extracting light curves from the TESS Full-Frame Images
During its two year prime mission the Transiting Exoplanet Survey Satellite
(TESS) will perform a time-series photometric survey covering over 80% of the
sky. This survey comprises observations of 26 24 x 96 degree sectors that are
each monitored continuously for approximately 27 days. The main goal of TESS is
to find transiting planets around 200,000 pre-selected stars for which fixed
aperture photometry is recorded every two minutes. However, TESS is also
recording and delivering Full-Frame Images (FFIs) of each detector at a 30
minute cadence. We have created an open-source tool, eleanor, to produce light
curves for objects in the TESS FFIs. Here, we describe the methods used in
eleanor to produce light curves that are optimized for planet searches. The
tool performs background subtraction, aperture and PSF photometry,
decorrelation of instrument systematics, and cotrending using principal
component analysis. We recover known transiting exoplanets in the FFIs to
validate the pipeline and perform a limited search for new planet candidates in
Sector 1. Our tests indicate that eleanor produces light curves with
significantly less scatter than other tools that have been used in the
literature. Cadence-stacked images, and raw and detrended eleanor light curves
for each analyzed star will be hosted on MAST, with planet candidates on
ExoFOP-TESS as Community TESS Objects of Interest (CTOIs). This work confirms
the promise that the TESS FFIs will enable the detection of thousands of new
exoplanets and a broad range of time domain astrophysics.Comment: 21 pages, 13 figures, 2 tables, Accepted to PAS
A Novel Framework for Highlight Reflectance Transformation Imaging
We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa
Validation of Advanced EM Models for UXO Discrimination
The work reported here details basic validation of our advanced physics-based
EMI forward and inverse models against data collected by the NRL TEMTADS
system. The data was collected under laboratory-type conditions using both
artificial spheroidal targets and real UXO. The artificial target models are
essentially exact, and enable detailed comparison of theory and data in support
of measurement platform characterization and target identification. Real UXO
targets cannot be treated exactly, but it is demonstrated that quantitative
comparisons of the data with the spheroid models nevertheless aids in
extracting key target discrimination information, such as target geometry and
hollow target shell thickness.Comment: 15 pages, 16 figure
Planet transit and stellar granulation detection with interferometry
Aims. We used realistic three-dimensional (3D) radiative hydrodynamical (RHD)
simulations from the Stagger-grid and synthetic images computed with the
radiative transfer code Optim3D to provide interferometric observables to
extract the signature of stellar granulation and transiting planets. Methods.
We computed intensity maps from RHD simulations for twelve interferometric
instruments covering wavelengths ranging from optical to infrared. The stellar
surface asymmetries in the brightness distribution mostly affect closure
phases. We compared the closure phases of the system star with a transiting
planet and the star alone and considered the impact of magnetic spots
constructing a hypothetical starspots image. Results. All the simulations show
departure from the axisymmetric case at all wavelengths. We presented two
possible targets (Beta Com and Procyon) and found that departures up to 16 deg
can be detected on the 3rd lobe and higher. In particular, MIRC is the most
appropriate instrument because it combines good UV coverage and long baselines.
Moreover, we explored the impact of convection on interferometric planet
signature for three prototypes of planets. It is possible to disentangle the
signature of the planet at particular wavelengths (either in the infrared or in
the optical) by comparing the closure phases of the star at difference phases
of the planetary transit. Conclusions. The detection and characterisation of
planets must be based on a comprehensive knowledge of the host star; this
includes the detailed study of the stellar surface convection with
interferometric techniques. In this context, RHD simulations are crucial to
reach this aim. We emphasize that interferometric observations should be pushed
at high spatial frequencies by accumulating observations on closure phases at
short and long baselines.Comment: accepted in Astronomy and Astrophysics, 13 pages. Some figures have
reduced resolution to decrease the size of the output file. Please contact
[email protected] to have the high resolution version of the pape
Radar Cross Section of Orbital Debris Objects
This discussion is concerned with the radar-data analysis and usage involved in the building of model orbital debris (OD) populations in the near-Earth environment, focusing on radar cross section (RCS). While varying with radar wavelength, physical dimension, material composition, overall shape and structure, the RCS of an irregular object is also strongly dependent on its spatial orientation. The historical records of observed RCSs for cataloged OD objects in the Space Surveillance Network are usually distributed over an RCS range, forming respective characteristic patterns. The National Aeronautics and Space Administration (NASA) Size Estimation Model provides an empirical probability-density function of RCS as a function of effective diameter (or characteristic length), which makes it feasible to predict possible RCS distributions for a given model OD population and to link data with model from a statistical perspective. The discussion also includes application of the widely used method of moments (MoM) and the Generalized Multi-particle Mie-solution (GMM) in the prediction of the RCS of arbitrarily shaped objects. Theoretical calculation results for an aluminum cube are compared with corresponding experimental measurements
Automatic normal orientation in point clouds of building interiors
Orienting surface normals correctly and consistently is a fundamental problem
in geometry processing. Applications such as visualization, feature detection,
and geometry reconstruction often rely on the availability of correctly
oriented normals. Many existing approaches for automatic orientation of normals
on meshes or point clouds make severe assumptions on the input data or the
topology of the underlying object which are not applicable to real-world
measurements of urban scenes. In contrast, our approach is specifically
tailored to the challenging case of unstructured indoor point cloud scans of
multi-story, multi-room buildings. We evaluate the correctness and speed of our
approach on multiple real-world point cloud datasets
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