5,061 research outputs found
Hyperspectral Data Acquisition and Its Application for Face Recognition
Current face recognition systems are rife with serious challenges in uncontrolled conditions: e.g., unrestrained lighting, pose variations, accessories, etc. Hyperspectral imaging (HI) is typically employed to counter many of those challenges, by incorporating the spectral information within different bands. Although numerous methods based on hyperspectral imaging have been developed for face recognition with promising results, three fundamental challenges remain: 1) low signal to noise ratios and low intensity values in the bands of the hyperspectral image specifically near blue bands; 2) high dimensionality of hyperspectral data; and 3) inter-band misalignment (IBM) correlated with subject motion during data acquisition.
This dissertation concentrates mainly on addressing the aforementioned challenges in HI. First, to address low quality of the bands of the hyperspectral image, we utilize a custom light source that has more radiant power at shorter wavelengths and properly adjust camera exposure times corresponding to lower transmittance of the filter and lower radiant power of our light source.
Second, the high dimensionality of spectral data imposes limitations on numerical analysis. As such, there is an emerging demand for robust data compression techniques with lows of less relevant information to manage real spectral data. To cope with these challenging problems, we describe a reduced-order data modeling technique based on local proper orthogonal decomposition in order to compute low-dimensional models by projecting high-dimensional clusters onto subspaces spanned by local reduced-order bases.
Third, we investigate 11 leading alignment approaches to address IBM correlated with subject motion during data acquisition. To overcome the limitations of the considered alignment approaches, we propose an accurate alignment approach ( A3) by incorporating the strengths of point correspondence and a low-rank model. In addition, we develop two qualitative prediction models to assess the alignment quality of hyperspectral images in determining improved alignment among the conducted alignment approaches. Finally, we show that the proposed alignment approach leads to promising improvement on face recognition performance of a probabilistic linear discriminant analysis approach
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit
The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload
onboard the satellite TechDemoSat-1, used to study the radiation environment in
Low Earth Orbit (635km). LUCID operated from 2014 to 2017, collecting
over 2.1 million frames of radiation data from its five Timepix detectors on
board. LUCID is one of the first uses of the Timepix detector technology in
open space, with the data providing useful insight into the performance of this
technology in new environments. It provides high-sensitivity imaging
measurements of the mixed radiation field, with a wide dynamic range in terms
of spectral response, particle type and direction. The data has been analysed
using computing resources provided by GridPP, with a new machine learning
algorithm that uses the Tensorflow framework. This algorithm provides a new
approach to processing Medipix data, using a training set of human labelled
tracks, providing greater particle classification accuracy than other
algorithms. For managing the LUCID data, we have developed an online platform
called Timepix Analysis Platform at School (TAPAS). This provides a swift and
simple way for users to analyse data that they collect using Timepix detectors
from both LUCID and other experiments. We also present some possible future
uses of the LUCID data and Medipix detectors in space.Comment: Accepted for publication in Advances in Space Researc
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