46,786 research outputs found
Deep Perceptual Mapping for Thermal to Visible Face Recognition
Cross modal face matching between the thermal and visible spectrum is a much
de- sired capability for night-time surveillance and security applications. Due
to a very large modality gap, thermal-to-visible face recognition is one of the
most challenging face matching problem. In this paper, we present an approach
to bridge this modality gap by a significant margin. Our approach captures the
highly non-linear relationship be- tween the two modalities by using a deep
neural network. Our model attempts to learn a non-linear mapping from visible
to thermal spectrum while preserving the identity in- formation. We show
substantive performance improvement on a difficult thermal-visible face
dataset. The presented approach improves the state-of-the-art by more than 10%
in terms of Rank-1 identification and bridge the drop in performance due to the
modality gap by more than 40%.Comment: BMVC 2015 (oral
Study of optical techniques for the Ames unitary wind tunnel: Digital image processing, part 6
A survey of digital image processing techniques and processing systems for aerodynamic images has been conducted. These images covered many types of flows and were generated by many types of flow diagnostics. These include laser vapor screens, infrared cameras, laser holographic interferometry, Schlieren, and luminescent paints. Some general digital image processing systems, imaging networks, optical sensors, and image computing chips were briefly reviewed. Possible digital imaging network systems for the Ames Unitary Wind Tunnel were explored
Towards dense object tracking in a 2D honeybee hive
From human crowds to cells in tissue, the detection and efficient tracking of
multiple objects in dense configurations is an important and unsolved problem.
In the past, limitations of image analysis have restricted studies of dense
groups to tracking a single or subset of marked individuals, or to
coarse-grained group-level dynamics, all of which yield incomplete information.
Here, we combine convolutional neural networks (CNNs) with the model
environment of a honeybee hive to automatically recognize all individuals in a
dense group from raw image data. We create new, adapted individual labeling and
use the segmentation architecture U-Net with a loss function dependent on both
object identity and orientation. We additionally exploit temporal regularities
of the video recording in a recurrent manner and achieve near human-level
performance while reducing the network size by 94% compared to the original
U-Net architecture. Given our novel application of CNNs, we generate extensive
problem-specific image data in which labeled examples are produced through a
custom interface with Amazon Mechanical Turk. This dataset contains over
375,000 labeled bee instances across 720 video frames at 2 FPS, representing an
extensive resource for the development and testing of tracking methods. We
correctly detect 96% of individuals with a location error of ~7% of a typical
body dimension, and orientation error of 12 degrees, approximating the
variability of human raters. Our results provide an important step towards
efficient image-based dense object tracking by allowing for the accurate
determination of object location and orientation across time-series image data
efficiently within one network architecture.Comment: 15 pages, including supplementary figures. 1 supplemental movie
available as an ancillary fil
TRIDENT: an Infrared Differential Imaging Camera Optimized for the Detection of Methanated Substellar Companions
A near-infrared camera in use at the Canada-France-Hawaii Telescope (CFHT)
and at the 1.6-m telescope of the Observatoire du Mont-Megantic is described.
The camera is based on a Hawaii-1 1024x1024 HgCdTe array detector. Its main
feature is to acquire three simultaneous images at three wavelengths across the
methane absorption bandhead at 1.6 microns, enabling, in theory, an accurate
subtraction of the stellar point spread function (PSF) and the detection of
faint close methanated companions. The instrument has no coronagraph and
features fast data acquisition, yielding high observing efficiency on bright
stars. The performance of the instrument is described, and it is illustrated by
laboratory tests and CFHT observations of the nearby stars GL526, Ups And and
Chi And. TRIDENT can detect (6 sigma) a methanated companion with delta H = 9.5
at 0.5" separation from the star in one hour of observing time. Non-common path
aberrations and amplitude modulation differences between the three optical
paths are likely to be the limiting factors preventing further PSF attenuation.
Instrument rotation and reference star subtraction improve the detection limit
by a factor of 2 and 4 respectively. A PSF noise attenuation model is presented
to estimate the non-common path wavefront difference effect on PSF subtraction
performance.Comment: 41 pages, 16 figures, accepted for publication in PAS
Counterfeit Detection with Multispectral Imaging
Multispectral imaging is becoming more practical for a variety of applications due to its ability to provide hyper specific information through a non-destructive analysis. Multispectral imaging cameras can detect light reflectance from different spectral bands of visible and nonvisible wavelengths. Based on the different amount of band reflectance, information can be deduced on the subject. Counterfeit detection applications of multispectral imaging will be decomposed and analyzed in this thesis. Relations between light reflectance and objects’ features will be addressed. The process of the analysis will be broken down to show how this information can be used to provide more insight on the object. This technology provides desired and viable information that can greatly improve multiple fields. For this paper, the multispectral imaging research process of element solution concentrations and counterfeit detection applications of multispectral imaging will be discussed. BaySpec’s OCI-M Ultra Compact Multispectral Imager is used for data collection. This camera is capable of capturing light reflectance from wavelengths of 400 – 1000 nm. Further research opportunities of developing self-automated unmanned aerial vehicles for precision agriculture and extending counterfeit detection applications will also be explored
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