Wide-field photometry is prone to various degradations, such as atmospheric ex- tinction, varying point spread functions, and aliasing in addition to classical noise sources such as photon, sky background, readout, and thermal noise. While space- borne observations do not suer from atmospheric eects, varying star images over a large sensor and aliasing may seriously impede good results. A measure of the achievable precision of ground-based dierential photometry with the prototype photometer for the BRITE satellite mission is reported, using real sky observa- tions. The data were obtained with the photometer attached to a paramount tracking platform, using the Image Reduction and Analysis Facility Software (IRAF) image reduction and analysis methods as well as the author's own Matlab Code. Special emphasis is placed on the analysis of varying apertures for vary- ing point spread functions, which shows that the accuracy can be improved by taking into account the statistics for each star instead of using a xed aperture. In addition a function is dened, which describes the expected error in terms of instrumental magnitudes, taking into account Poisson distributed noise and mag- nitude independent noise, mainly aliasing. This function is then t to observed data in a two-dimensional least squares sense, providing a calculated aliasing error of 7 millimagnitudes. This function is furthermore rewritten in terms of the stan- dard magnitude B. A maximum magnitude can then be determined for a certain precision, which shows that the Bright Target Explorer (BRITE) can reach a pho- tometric error of 1 millimagnitude for stars with magnitude B &lt; 3:5, assuming the worst case duty cycle of 15 minutes. / <p>Validerat; 20110211 (anonymous)</p>

Use of imaging systems for performing various situational awareness tasks in militaryand commercial settings has a long history. There is increasing recognition,however, that a much better job can be done by developing non-traditional opticalsystems that exploit the task-specific system aspects within the imager itself. Insome cases, a direct consequence of this approach can be real-time data compressionalong with increased measurement fidelity of the task-specific features. In others,compression can potentially allow us to perform high-level tasks such as direct trackingusing the compressed measurements without reconstructing the scene of interest.In this dissertation we present novel advancements in feature-specific (FS) imagersfor large field-of-view surveillence, and estimation of temporal object-scene changesutilizing the compressive imaging paradigm. We develop these two ideas in parallel.In the first case we show a feature-specific (FS) imager that optically multiplexesmultiple, encoded sub-fields of view onto a common focal plane. Sub-field encodingenables target tracking by creating a unique connection between target characteristicsin superposition space and the target's true position in real space. This isaccomplished without reconstructing a conventional image of the large field of view.System performance is evaluated in terms of two criteria: average decoding time andprobability of decoding error. We study these performance criteria as a functionof resolution in the encoding scheme and signal-to-noise ratio. We also includesimulation and experimental results demonstrating our novel tracking method. Inthe second case we present a FS imager for estimating temporal changes in the objectscene over time by quantifying these changes through a sequence of differenceimages. The difference images are estimated by taking compressive measurementsof the scene. Our goals are twofold. First, to design the optimal sensing matrixfor taking compressive measurements. In scenarios where such sensing matrices arenot tractable, we consider plausible candidate sensing matrices that either use theavailable <italic>a priori</italic> information or are non-adaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We present results to show the efficacy of these techniques and discuss the advantages of each.

Retinal imaging enables the visualization of a portion of the human microvasculature in-vivo and non-invasively. The scanning laser ophthalmoscope (SLO), provides images characterized by an ultra-wide field of view (UWFoV) covering approximately 180-200º in a single scan, minimizing the discomfort for the subject. The microvasculature visible in retinal images and its changes have been vastly investigated as candidate biomarkers for different types of systemic conditions like cardiovascular disease (CVD), which currently remains the main cause of death in Europe. For the CARMEN study, UWFoV SLO images were acquired from more than 1,000 people who were recruited from two studies, TASCFORCE and SCOT-HEART, focused on CVD. This thesis presents an automated system for SLO image processing and computation of candidate biomarkers to be associated with cardiovascular risk and MRI imaging data. A vessel segmentation technique was developed by making use of a bank of multi-scale matched filters and a neural network classifier. The technique was devised to minimize errors in vessel width estimation, in order to ensure the reliability of width measures obtained from the vessel maps. After a step of refinement of the centrelines, a multi-level classification technique was deployed to label all vessel segments as arterioles or venules. The method exploited a set of pixel-level features for local classification and a novel formulation for a graph cut approach to partition consistently the retinal vasculature that was modelled as an undirected graph. Once all the vessels were labelled, a tree representation was adopted for each vessel and its branches to fully automate the process of biomarker extraction. Finally, a set of 75 retinal parameters, including information provided by the periphery of the retina, was created for each image and used for the biomarker investigation.