Knowledge acquisition is concerned with finding and structuring knowledge in such a way that it can be used in a variety of intelligent decision-making tools. Knowledge of a domain can be encoded as taxonomy i.e., a hierarchically organized set of categories. The relationships within the hierarchy can be of different kinds, depending on the application, and a typical taxonomy includes several different kinds of relations. Thus taxonomies play an important role in analyzing and modeling knowledge. The focus of this study is to derive knowledge from a standard taxonomic structure in the remote sensing domain. The various methodological channels adopted by the remote sensing data analysts to produce different information products normally go through some definite processes, which can be examined along with their context (spectral, spatial, temporal) by the taxonomical approach. This allows users to assess the applicability of a methodology for a particular area of interest and also has the advantage in aiding the upper-level decision-makers in understanding why different approaches might provide different outputs to the same source data. Some of the previous work done by a number of multi disciplinary researchers in analyzing remote sensing data has been used in this study to examine the structure of their methodologies from a taxonomical perspective. The analysis of the developed taxonomies clearly indicates a definite structure to the underlying analysis procedures and has potential for the development of systems to automate them.

<p>Quantitative imaging is important in medical imaging. Physical phantoms are used. There is reason to believe that anthropomorphic physical phatoms are better than uniform phantoms. To investigate this question, we develop a novel imaging metrology with a phatient-based phantom and apply its use to several digital breast tomosytneshis machines. At the same time, we use the traditional means of assessing image quality. Our results show a strong dependence on image performance with the type of phantom used. Furthermore, we demonstrate the feasibility of this metrology in real, clinical applications.</p> / Dissertation

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Video Image Analysis (VIA) is a digital camera based technology that extracts relevant information from images using purpose tailored image processing software. In the present work, the VSS2000 image analysis system from E+V Technology GmbH has been used in a large lamb abattoir to determine the value of carcasses in an objective, consistent and automated way. In this thesis results are reported of several experiments conducted within the framework of two UK-funded projects. The aims of the research were (i) the calibration and validation of the VIAtechnique for the evaluation of lamb carcasses under UK abattoir conditions, with the view to scientifically examine the accuracy and precision of information from the VIA systems as the basis for a value-based marketing system, (ii) to investigate the use of VIA measurements (weights of primal meat yields and carcass dimensional measurements) in sheep breeding programmes to improve carcass and meat quality and (iii) to evaluate the potential of this technology to reward increased carcass quality associated with the use of breeding strategies based on the inclusion of a quantitative trait locus (QTL) for improved muscularity. Accuracy, precision and consistency of The Meat and Livestock Commission (MLC) carcass classification scheme, currently used in UK abattoirs to evaluate carcass quality, was compared against the VIA system in the prediction of various primal joint weights. The results highlighted the advantage of the VIA system being on average 2% more accurate (measured as coefficient of determination: R2) and 12% more precise (measured as root meat squared error: RMSE) in predicting weight of primal meat yields (leg, chump, loin, breast and shoulder) of the lamb carcasses than the MLC carcass classification scheme. The genetic analysis of VIA-based predicted primal joint weights showed substantial additive genetic variance, suggesting that their use in sheep breeding programmes could improve carcass quality either by an improvement of conformation or by an increased weight of the most valuable primal cuts, without an increase in fatness. Favourable associations between VIA primal weights and performance traits indicate that selection based on VIA traits is possible without a negative effect on average daily gain, live weight and cold carcass weight. Although computer tomography (CT) and dissection found in related studies significant effects of a Texel muscling-QTL (TM-QTL) for increased muscularity in the loin region, in the present study they could not be identified by both, the current industry carcass evaluation system for conformation and fatness and the VIA system. A calibration of the VIA system against CT measurements resulted in improved VIA prediction equations for primal meat yields and also showed a moderate potential to estimate loin muscle traits measured by CT and to detect partially the effect of the TM-QTL on these traits. The results of the research demonstrated that VIA is a consistent method to measure carcass composition and that it improved the prediction (accuracy and precision) of primal meat yields compared to the present MLC scoring system. The estimated genetic parameters for VIA primal meat yields suggested that selection for increased lean meat yield from lamb carcass measured using VIA can contribute to genetic improvement of carcass quality without increasing carcass fatness. The results suggest that VIA technology installed in abattoirs could provide the means for the development of a value-based marketing system by paying for weights of the most valuable primal cuts measured using VIA.

Deciphering the mechanisms that underlie circuit function in the hippocampal formation is a key challenge for neuroscience. This region, which includes the medial entorhinal cortex (MEC), is critical for spatial learning and episodic memory in humans. Spatially modulated cells in the MEC, the grid cells, provide a topographical representation of space, but we are yet to establish the neuronal properties that underlie this or the contribution that particular cells in different regions of the MEC and hippocampus make to circuit function. This is partially because the specific targeting of the network with genetic tools is complicated by a multitude of cell types with predominantly unknown molecular profiles. To address our limited understanding of the molecular organisation of the MEC, I have characterised how the expression of genes is distributed throughout different layers of the MEC, using a custom-designed resource that facilitates analysis of in situ hybridisation data from the Allen Brain Atlas. Through simultaneous extraction of gene expression data across thousands of 2D aligned images, I reveal striking differences between layers within MEC, demonstrating that layer II contains the highest proportion of genes enriched in a single layer, whereas gene expression is very rarely confined to layer III. Of particular interest, layer II of MEC is highly enriched for Alzheimer’s disease pathway genes, providing insight into its vulnerability as one of the first brain regions to show pathology. I also identify over 1000 genes that are expressed with a dorso-ventral gradient that maps onto the topographic organisation of MEC connectivity, grid cell spatial resolution and synaptic integrative properties of cells. An intriguing group of genes that closely relate circuit activity to gene expression, the plasticity-related activity-dependent genes, often show this pattern of expression. Focussing on the activity-dependent expression of one such activity-regulated, plasticity-related gene, Arc, I provide a novel view of MEC function. During simple novel exploration, Arc expression is up-regulated to a much greater extent in the deep layers of dorsal MEC than in the grid cell-rich superficial layers. By selectively disrupting the predominant hippocampal input to dorsal MEC, which terminates in the deep layers, I show that the significance of this up-regulation is independent of hippocampal inputs. Thus, although research addressing MEC function is particularly focussed on the superficial layers, during the exploratory behaviour that potentially primes the system for representing an environment, important plasticity may be occurring at the synapses onto deep layer neurons. In summary, my investigations of baseline and activity-dependent gene expression in MEC have revealed a molecular organisation both across different layers and along a functionally relevant gradient. This may be important for specifically targeting microcircuits in MEC and for characterising how laminar and regional differences contribute to the encoding of space in the hippocampal formation.

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När en kock komponerar en maträtt som skall fotograferas kommer han behöva göra en medveten estetisk bedömning innan maträtten tillagas, för att sedan analysera och förstå hur betraktaren uppfattar matfotografi. I denna litteraturstudie är syftet att undersöka hur estetiska begrepp och bildanalysmetoder tillämpas inom matfotografi. Uppsatsen bygger på en litteraturstudie, där fyra vetenskapliga artiklar har tillämpats för att besvara syftet. Resultatet i litteraturstudien visar att det är svårt att finna en metod att applicera på matfotografi, men om det går att skapa ett språk med flera metoder tillsammans finns det stora fördelar ur ett analytiskt perspektiv. Tillämpningen av estetiska begrepp tillsammans med bildanalyser är komplicerat och forskningen som gjorts är inte tillräcklig / B-uppsatser