232,751 research outputs found
Self-coherent camera as a focal plane wavefront sensor: simulations
Direct detection of exoplanets requires high dynamic range imaging.
Coronagraphs could be the solution, but their performance in space is limited
by wavefront errors (manufacturing errors on optics, temperature variations,
etc.), which create quasi-static stellar speckles in the final image. Several
solutions have been suggested for tackling this speckle noise. Differential
imaging techniques substract a reference image to the coronagraphic residue in
a post-processing imaging. Other techniques attempt to actively correct
wavefront errors using a deformable mirror. In that case, wavefront aberrations
have to be measured in the science image to extremely high accuracy. We propose
the self-coherent camera sequentially used as a focal-plane wavefront sensor
for active correction and differential imaging. For both uses, stellar speckles
are spatially encoded in the science image so that differential aberrations are
strongly minimized. The encoding is based on the principle of light incoherence
between the hosting star and its environment. In this paper, we first discuss
one intrinsic limitation of deformable mirrors. Then, several parameters of the
self-coherent camera are studied in detail. We also propose an easy and robust
design to associate the self-coherent camera with a coronagraph that uses a
Lyot stop. Finally, we discuss the case of the association with a four-quadrant
phase mask and numerically demonstrate that such a device enables the detection
of Earth-like planets under realistic conditions. The parametric study of the
technique lets us believe it can be implemented quite easily in future
instruments dedicated to direct imaging of exoplanets.Comment: 15 pages, 14 figures, accepted in A&A (here is the final version
High-speed Video from Asynchronous Camera Array
This paper presents a method for capturing high-speed video using an
asynchronous camera array. Our method sequentially fires each sensor in a
camera array with a small time offset and assembles captured frames into a
high-speed video according to the time stamps. The resulting video, however,
suffers from parallax jittering caused by the viewpoint difference among
sensors in the camera array. To address this problem, we develop a dedicated
novel view synthesis algorithm that transforms the video frames as if they were
captured by a single reference sensor. Specifically, for any frame from a
non-reference sensor, we find the two temporally neighboring frames captured by
the reference sensor. Using these three frames, we render a new frame with the
same time stamp as the non-reference frame but from the viewpoint of the
reference sensor. Specifically, we segment these frames into super-pixels and
then apply local content-preserving warping to warp them to form the new frame.
We employ a multi-label Markov Random Field method to blend these warped
frames. Our experiments show that our method can produce high-quality and
high-speed video of a wide variety of scenes with large parallax, scene
dynamics, and camera motion and outperforms several baseline and
state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201
Monitoring wild animal communities with arrays of motion sensitive camera traps
Studying animal movement and distribution is of critical importance to
addressing environmental challenges including invasive species, infectious
diseases, climate and land-use change. Motion sensitive camera traps offer a
visual sensor to record the presence of a broad range of species providing
location -specific information on movement and behavior. Modern digital camera
traps that record video present new analytical opportunities, but also new data
management challenges. This paper describes our experience with a terrestrial
animal monitoring system at Barro Colorado Island, Panama. Our camera network
captured the spatio-temporal dynamics of terrestrial bird and mammal activity
at the site - data relevant to immediate science questions, and long-term
conservation issues. We believe that the experience gained and lessons learned
during our year long deployment and testing of the camera traps as well as the
developed solutions are applicable to broader sensor network applications and
are valuable for the advancement of the sensor network research. We suggest
that the continued development of these hardware, software, and analytical
tools, in concert, offer an exciting sensor-network solution to monitoring of
animal populations which could realistically scale over larger areas and time
spans
Rank-based camera spectral sensitivity estimation
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the labâa difficult and lengthy procedureâor through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have âraw mode.â Experiments validate our method
A multi-modal event detection system for river and coastal marine monitoring applications
AbstractâThis work is investigating the use of a multi-modal
sensor network where visual sensors such as cameras and
satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network
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