8,464 research outputs found
Direct Observation of Cosmic Strings via their Strong Gravitational Lensing Effect: II. Results from the HST/ACS Image Archive
We have searched 4.5 square degrees of archival HST/ACS images for cosmic
strings, identifying close pairs of similar, faint galaxies and selecting
groups whose alignment is consistent with gravitational lensing by a long,
straight string. We find no evidence for cosmic strings in five large-area HST
treasury surveys (covering a total of 2.22 square degrees), or in any of 346
multi-filter guest observer images (1.18 square degrees). Assuming that
simulations ccurately predict the number of cosmic strings in the universe,
this non-detection allows us to place upper limits on the unitless Universal
cosmic string tension of G mu/c^2 < 2.3 x 10^-6, and cosmic string density of
Omega_s < 2.1 x 10^-5 at the 95% confidence level (marginalising over the other
parameter in each case). We find four dubious cosmic string candidates in 318
single filter guest observer images (1.08 square degrees), which we are unable
to conclusively eliminate with existing data. The confirmation of any one of
these candidates as cosmic strings would imply G mu/c^2 ~ 10^-6 and Omega_s ~
10^-5. However, we estimate that there is at least a 92% chance that these
string candidates are random alignments of galaxies. If we assume that these
candidates are indeed false detections, our final limits on G mu/c^2 and
Omega_s fall to 6.5 x 10^-7 and 7.3 x 10^-6. Due to the extensive sky coverage
of the HST/ACS image archive, the above limits are universal. They are quite
sensitive to the number of fields being searched, and could be further reduced
by more than a factor of two using forthcoming HST data.Comment: 21 pages, 18 figure
Spotlight the Negatives: A Generalized Discriminative Latent Model
Discriminative latent variable models (LVM) are frequently applied to various
visual recognition tasks. In these systems the latent (hidden) variables
provide a formalism for modeling structured variation of visual features.
Conventionally, latent variables are de- fined on the variation of the
foreground (positive) class. In this work we augment LVMs to include negative
latent variables corresponding to the background class. We formalize the
scoring function of such a generalized LVM (GLVM). Then we discuss a framework
for learning a model based on the GLVM scoring function. We theoretically
showcase how some of the current visual recognition methods can benefit from
this generalization. Finally, we experiment on a generalized form of Deformable
Part Models with negative latent variables and show significant improvements on
two different detection tasks.Comment: Published in proceedings of BMVC 201
Image fusion techniqes for remote sensing applications
Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data
Optic nerve head segmentation
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image
Long gravitational-wave transients and associated detection strategies for a network of terrestrial interferometers
Searches for gravitational waves (GWs) traditionally focus on persistent sources (e.g., pulsars or the stochastic background) or on transients sources (e.g., compact binary inspirals or core-collapse supernovae), which last for time scales of milliseconds to seconds. We explore the possibility of long GW transients with unknown waveforms lasting from many seconds to weeks. We propose a novel analysis technique to bridge the gap between short O(s) “burst” analyses and persistent stochastic analyses. Our technique utilizes frequency-time maps of GW strain cross power between two spatially separated terrestrial GW detectors. The application of our cross power statistic to searches for GW transients is framed as a pattern recognition problem, and we discuss several pattern-recognition techniques. We demonstrate these techniques by recovering simulated GW signals in simulated detector noise. We also recover environmental noise artifacts, thereby demonstrating a novel technique for the identification of such artifacts in GW interferometers. We compare the efficiency of this framework to other techniques such as matched filtering
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