The art of reconstructing a real-world scene digitally has been on the mind of researchers for decades. Recently, it has attracted more and more attention from companies seeing a chance to bring this kind of technology to the market. Digital reconstruction of buildings in particular is a niche that has both potential and room for improvement. With this background, this thesis will present the design and evaluation of a pipeline made to find and correct approximately flat surfaces in architectural scenes. The scenes are 3D-reconstructed triangle meshes based on RGB images. The thesis will also comprise an evaluation of a few different components available for doing this, leading to a choice of best components. The goal is to improve the visual quality of the reconstruction. The final pipeline is designed with two blocks - one to detect initial plane seeds and one to refine the detected planes. The first block makes use of a multi-label energy formulation on the graph that describes the reconstructed surface. Penalties are assigned to each vertex and each edge of the graph based on the vertex labels, effectively describing a Markov Random Field. The energy is minimized with the help of the alpha-expansion algorithm. The second block uses heuristics for growing the detected plane seeds, merging similar planes together and extracting deviating details. Results on several scenes are presented, showing that the visual quality has been improved while maintaining accuracy compared with ground truth data. / Konsten att digitalt rekonstruera en verklig miljö har länge varit intressant för forskare. Nyligen har området även tilldragit sig mer och mer uppmärksamhet från företag som ser en möjlighet att föra den här typen av teknik till produkter på marknaden. I synnerhet är digital rekonstruktion av byggnader en nisch som har både stor potential och möjlighet till förbättring. Med denna bakgrund så presenterar detta examensarbete designen för och utvärderingen av en pipeline som skapats för att detektera och rätta till approximativt platta regioner i arkitektoniska miljöer. Miljöerna är 3D-rekonstruerade triangelmeshar skapade från RGB-bilder. Examensarbetet omfattar även utvärdering av olika komponenter för att uppnå detta, som avslutas med att de mest lämpliga komponenterna presenteras. Målet i korthet är att förbättra den visuella kvaliteten av en rekonstruerad modell. Den slutgiltiga pipelinen består av två övergripande block - ett för att detektera initiala plan och ett för att förbättra de funna planen. Det första blocket använder en multi-label energiformulering på grafen som beskriver den rekonstruerade ytan. Straffvärden tilldelas varje vertex och varje båge i grafen baserade på varje vertex label. På så sätt beskriver grafen ett Markov Random Field. Energin är sedan minimerad med alpha-expansion-algoritmen. Det andra blocket använder heuristiker för att låta planen växa, slå ihop närliggande plan och för att extrahera avvikande detaljer. Resultat på flera miljöer presenteras också för att påvisa att den visuella kvaliteten har förbättrats utan att rekonstruktionens noggrannhet har försämrats jämfört med ground truth-data.

Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona. Recipient of the 2016 Katheryne B. Willock Library Research Award. / The nervous system is composed of two types of cells: neurons and glia. In neuronal circuits, neurons communicate through synapses and glia play a crucial modulatory role. To modulate chemical reuptake, glia send processes close to synapses and many glia directly appose or ensheathe a synapse. This structural motif is one of the elements often included in describing a vertebrate tripartite synapse, which includes a bidirectional functional neuron-glia relationship. The exact nature of this neuron-glia communication is not well understood. In the invertebrate fruit fly, we have also found that particular neurons and glia also have a bidirectional functional relationship. This allows us to ask new questions about glial morphology. Throughout multiple images, I identified particular neuronal synapses and surrounding glia. After creating a 3D reconstruction, I measured the distance between a particular neuronal synapse and its closest glial process. Interestingly, the neuronal synapses were not directly apposed or ensheathed by glia, and the distance to the closest glial process varied one-hundred-fold. With variable distance, functional communication is consistently present. These findings provide important insight into invertebrate neuron-glia communication, and offer new avenues to investigate the structural neuron-glia relationships that are required for reciprocal signaling between the two cell classes.

This thesis looks into the examination of polymer composite wreckage from the perspective of the aircraft accident investigator. It develops an understanding of the process of wreckage examination as well as identifying the potential for visual and macroscopic interpretation of polymer composite aircraft wreckage. The in-field examination of aircraft wreckage, and subsequent interpretations of material failures, can be a significant part of an aircraft accident investigation. As the use of composite materials in aircraft construction increases, the understanding of how macroscopic failure characteristics of composite materials may aid the field investigator is becoming of increasing importance. The first phase of this research project was to explore how investigation practitioners conduct wreckage examinations. Four accident investigation case studies were examined. The analysis of the case studies provided a framework of the wreckage examination process. Subsequently, a literature survey was conducted to establish the current level of knowledge on the visual and macroscopic interpretation of polymer composite failures. Relevant literature was identified and a compendium of visual and macroscopic characteristics was created. Two full-scale polymer composite wing structures were loaded statically, in an upward bending direction, until each wing structure fractured and separated. The wing structures were subsequently examined for the existence of failure characteristics. The examination revealed that whilst characteristics were present, the fragmentation of the structure destroyed valuable evidence. A hypothetical accident scenario utilising the fractured wing structures was developed, which UK government accident investigators subsequently investigated. This provided refinement to the investigative framework and suggested further guidance on the interpretation of polymer composite failures by accident investigators.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Mathematics / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Signal and image processing is important in a wide range of areas, including medical and astronomical imaging, and speech and acoustic signal processing. There is often a need for the reconstruction of these objects to be very fast, as they have some cost (perhaps a monetary cost, although often it is a time cost) attached to them. This work considers the development of algorithms that allow these signals and images to be reconstructed quickly and without perceptual quality loss. The main problem considered here is that of reducing the amount of time needed for images to be reconstructed, by decreasing the amount of data necessary for a high quality image to be produced. In addressing this problem two basic ideas are considered. The first is a subset selection problem where the aim is to extract a subset of data, of a predetermined size, from a much larger data set. To do this we first need some metric with which to measure how `good' (or how close to `best') a data subset is. Then, using this metric, we seek an algorithm that selects an appropriate data subset from which an accurate image can be reconstructed. Current algorithms use a criterion based upon the trace of a matrix. In this work we derive a simpler criterion based upon the determinant of a matrix. We construct two new algorithms based upon this new criterion and provide numerical results to demonstrate their accuracy and efficiency. A row exchange strategy is also described, which takes a given subset and performs interchanges to improve the quality of the selected subset. The second idea is, given a reduced set of data, how can we quickly reconstruct an accurate signal or image? Compressed sensing provides a mathematical framework that explains that if a signal or image is known to be sparse relative to some basis, then it may be accurately reconstructed from a reduced set of data measurements. The reconstruction process can be posed as a convex optimization problem. We introduce an algorithm that aims to solve the corresponding problem and accurately reconstruct the desired signal or image. The algorithm is based upon the Barzilai-Borwein algorithm and tailored specifically to the compressed sensing framework. Numerical experiments show that the algorithm is competitive with currently used algorithms. Following the success of compressed sensing for sparse signal reconstruction, we consider whether it is possible to reconstruct other signals with certain structures from reduced data sets. Specifically, signals that are a combination of a piecewise constant part and a sparse component are considered. A reconstruction process for signals of this type is detailed and numerical results are presented.

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