4,558 research outputs found
Finding strong lenses in CFHTLS using convolutional neural networks
We train and apply convolutional neural networks, a machine learning
technique developed to learn from and classify image data, to
Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the
identification of potential strong lensing systems. An ensemble of four
convolutional neural networks was trained on images of simulated galaxy-galaxy
lenses. The training sets consisted of a total of 62,406 simulated lenses and
64,673 non-lens negative examples generated with two different methodologies.
The networks were able to learn the features of simulated lenses with accuracy
of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000
simulations. An ensemble of trained networks was applied to all of the 171
square degrees of the CFHTLS wide field image data, identifying 18,861
candidates including 63 known and 139 other potential lens candidates. A second
search of 1.4 million early type galaxies selected from the survey catalog as
potential deflectors, identified 2,465 candidates including 117 previously
known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266
novel probable or potential lenses and 2097 candidates we classify as false
positives. For the catalog-based search we estimate a completeness of 21-28%
with respect to detectable lenses and a purity of 15%, with a false-positive
rate of 1 in 671 images tested. We predict a human astronomer reviewing
candidates produced by the system would identify ~20 probable lenses and 100
possible lenses per hour in a sample selected by the robot. Convolutional
neural networks are therefore a promising tool for use in the search for lenses
in current and forthcoming surveys such as the Dark Energy Survey and the Large
Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA
Simultaneous multi-band detection of Low Surface Brightness galaxies with Markovian modelling
We present an algorithm for the detection of Low Surface Brightness (LSB)
galaxies in images, called MARSIAA (MARkovian Software for Image Analysis in
Astronomy), which is based on multi-scale Markovian modeling. MARSIAA can be
applied simultaneously to different bands. It segments an image into a
user-defined number of classes, according to their surface brightness and
surroundings - typically, one or two classes contain the LSB structures. We
have developed an algorithm, called DetectLSB, which allows the efficient
identification of LSB galaxies from among the candidate sources selected by
MARSIAA. To assess the robustness of our method, the method was applied to a
set of 18 B and I band images (covering 1.3 square degrees in total) of the
Virgo cluster. To further assess the completeness of the results of our method,
both MARSIAA, SExtractor, and DetectLSB were applied to search for (i) mock
Virgo LSB galaxies inserted into a set of deep Next Generation Virgo Survey
(NGVS) gri-band subimages and (ii) Virgo LSB galaxies identified by eye in a
full set of NGVS square degree gri images. MARSIAA/DetectLSB recovered ~20%
more mock LSB galaxies and ~40% more LSB galaxies identified by eye than
SExtractor/DetectLSB. With a 90% fraction of false positives from an entirely
unsupervised pipeline, a completeness of 90% is reached for sources with r_e >
3" at a mean surface brightness level of mu_g=27.7 mag/arcsec^2 and a central
surface brightness of mu^0 g=26.7 mag/arcsec^2. About 10% of the false
positives are artifacts, the rest being background galaxies. We have found our
method to be complementary to the application of matched filters and an
optimized use of SExtractor, and to have the following advantages: it is
scale-free, can be applied simultaneously to several bands, and is well adapted
for crowded regions on the sky.Comment: 39 pages, 18 figures, accepted for publication in A
Vision, Action, and Make-Perceive
In this paper, I critically assess the enactive account of visual perception recently defended by Alva Noë (2004). I argue inter alia that the enactive account falsely identifies an object’s apparent shape with its 2D perspectival shape; that it mistakenly assimilates visual shape perception and volumetric object recognition; and that it seriously misrepresents the constitutive role of bodily action in visual awareness. I argue further that noticing an object’s perspectival shape involves a hybrid experience combining both perceptual and imaginative elements – an act of what I call ‘make-perceive.
A preliminary approach to intelligent x-ray imaging for baggage inspection at airports
Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted
Structure motivator: a tool for exploring small three-dimensional elements in proteins
<br>Background:
Protein structures incorporate characteristic three-dimensional elements defined by some or all of hydrogen bonding, dihedral angles and amino acid sequence. The software application, Structure Motivator, allows interactive exploration and analysis of such elements, and their resolution into sub-classes.</br>
<br>Results:
Structure Motivator is a standalone application with an embedded relational database of proteins that, as a starting point, can furnish the user with a palette of unclassified small peptides or a choice of pre-classified structural motifs. Alternatively the application accepts files of data generated externally. After loading, the structural elements are displayed as two-dimensional plots of dihedral angles (φ/ψ, φ/χ1 or in combination) for each residue, with visualization options to allow the conformation or amino acid composition at one residue to be viewed in the context of that at other residues. Interactive selections may then be made and structural subsets saved to file for further sub-classification or external analysis. The application has been applied both to classical motifs, such as the β-turn, and ‘non-motif’ structural elements, such as specific segments of helices.</br>
<br>Conclusions:
Structure Motivator allows structural biologists, whether or not they possess computational skills, to subject small structural elements in proteins to rapid interactive analysis that would otherwise require complex programming or database queries. Within a broad group of structural motifs, it facilitates the identification and separation of sub-classes with distinct stereochemical properties.</br>
An active infrared thermography method for fiber orientation assessment of fiber-reinforced composite materials
Fiber orientation in composite materials is an important feature since the arrangement or orientation of the fibers relative to one another has a significant influence on the strength and other properties of fiber reinforced composites. In this paper we present a method to assess the fiber orientation on the surface of carbon fiber reinforced polymer (CFRP) laminates. More specifically, a diode-laser beam is used to locally heat a small spot on the surface of the sample. Observation of the heat pattern in the infrared spectrum enables the assessment of the fiber orientation. Different samples and different regions on the surface of the samples are tested in order to estimate the precision of the method
Automated Optical Inspection and Image Analysis of Superconducting Radio-Frequency Cavities
The inner surface of superconducting cavities plays a crucial role to achieve
highest accelerating fields and low losses. For an investigation of this inner
surface of more than 100 cavities within the cavity fabrication for the
European XFEL and the ILC HiGrade Research Project, an optical inspection robot
OBACHT was constructed. To analyze up to 2325 images per cavity, an image
processing and analysis code was developed and new variables to describe the
cavity surface were obtained. The accuracy of this code is up to 97% and the
PPV 99% within the resolution of 15.63 . The optical obtained
surface roughness is in agreement with standard profilometric methods. The
image analysis algorithm identified and quantified vendor specific fabrication
properties as the electron beam welding speed and the different surface
roughness due to the different chemical treatments. In addition, a correlation
of with a significance of between an obtained
surface variable and the maximal accelerating field was found
Nonlinear tube-fitting for the analysis of anatomical and functional structures
We are concerned with the estimation of the exterior surface and interior
summaries of tube-shaped anatomical structures. This interest is motivated by
two distinct scientific goals, one dealing with the distribution of HIV
microbicide in the colon and the other with measuring degradation in
white-matter tracts in the brain. Our problem is posed as the estimation of the
support of a distribution in three dimensions from a sample from that
distribution, possibly measured with error. We propose a novel tube-fitting
algorithm to construct such estimators. Further, we conduct a simulation study
to aid in the choice of a key parameter of the algorithm, and we test our
algorithm with validation study tailored to the motivating data sets. Finally,
we apply the tube-fitting algorithm to a colon image produced by single photon
emission computed tomography (SPECT) and to a white-matter tract image produced
using diffusion tensor imaging (DTI).Comment: Published in at http://dx.doi.org/10.1214/10-AOAS384 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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