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.

Due to the limited supply and perishable nature of blood products, effective management of blood collection is critical for high quality healthcare delivery. Whole blood is typically collected over a 6 to 8 hour collection window from volunteer donors at sites, e.g., schools, universities, churches, companies, that are a significant distance from the blood products processing facility and then transported from collection site to processing facility by a blood mobile. The length of time between collecting whole blood and processing it into cryoprecipitate ("cryo"), a critical blood product for controlling massive hemorrhaging, cannot take longer than 8 hours (the 8 hour collection to completion constraint), while the collection to completion constraint for other blood products is 24 hours. In order to meet the collection to completion constraint for cryo, it is often necessary to have a "mid-drive collection"; i.e., for a vehicle other than the blood mobile to pickup and transport, at extra cost, whole blood units collected during early in the collection window to the processing facility. In this dissertation, we develop analytical models to: (1) analyze which collection sites should be designated as cryo collection sites to minimize total collection costs while satisfying the collection to completion constraint and meeting the weekly production target (the non-split case), (2) analyze the impact of changing the current process to allow collection windows to be split into two intervals and then determining which intervals should be designated as cryo collection intervals (the split case), (3) insure that the weekly production target is met with high probability. These problems lead to MDP models with large state and action spaces and constraints to guarantee that the weekly production target is met with high probability. These models are computationally intractable for problems having state and action spaces of realistic cardinality. We consider two approaches to guarantee that the weekly production target is met with high probability: (1) a penalty function approach and (2) a chance constraint approach. For the MDP with penalty function approach, we first relax a constraint that significantly reduces the cardinality of the state space and provides a lower bound on the optimal expected weekly cost of collecting whole blood for cryo while satisfying the collection to completion constraint. We then present an action elimination procedure that coupled with the constraint relaxation leads to a computationally tractable lower bound. We then develop several heuristics that generate sub-optimal policies and provide an analytical description of the difference between the upper and lower bounds in order to determine the quality of the heuristics. For the multiple decision epoch MDP model with chance constraint approach, we first note by example that a straightforward application of dynamic programming can lead to a sub-optimal policy. We then restrict the model to a single decision epoch. We then use a computationally tractable rolling horizon procedure for policy determination. We also present a simple greedy heuristic (another rolling horizon decision making procedure) based on ranking the collection intervals by mid-drive pickup cost per unit of expected cryo collected, which results in a competitive sub-optimal solution and leads to the development of a practical decision support tool (DST). Using real data from the American Red Cross (ARC), we estimate that this DST reduces total cost by about 30% for the non-split case and 70% for the split case, compared to the current practice. Initial implementation of the DST at the ARC Southern regional manufacturing and service center supports our estimates and indicates the potential for significant improvement in current practice.

Markov chains offer a rigorous mathematical framework to describe systems that exhibit stochastic behaviour, as they are supported by a plethora of methodologies to analyse their properties. Stochastic process algebras are high-level formalisms, where systems are represented as collections of interacting components. This compositional approach to modelling allows us to describe complex Markov chains using a compact high-level specification. There is an increasing need to investigate the properties of complex systems, not only in the field of computer science, but also in computational biology. To explore the stochastic properties of large Markov chains is a demanding task in terms of computational resources. Approximating the stochastic properties can be an effective way to deal with the complexity of large models. In this thesis, we investigate methodologies to approximate the stochastic behaviour of Markovian process algebra models. The discussion revolves around two main topics: approximate state-space aggregation and stochastic simulation. Although these topics are different in nature, they are both motivated by the need to efficiently handle complex systems. Approximate Markov chain aggregation constitutes the formulation of a smaller Markov chain that approximates the behaviour of the original model. The principal hypothesis is that states that can be characterised as equivalent can be adequately represented as a single state. We discuss different notions of approximate state equivalence, and how each of these can be used as a criterion to partition the state-space accordingly. Nevertheless, approximate aggregation methods typically require an explicit representation of the transition matrix, a fact that renders them impractical for large models. We propose a compositional approach to aggregation, as a means to efficiently approximate complex Markov models that are defined in a process algebra specification, PEPA in particular. Regarding our contributions to Markov chain simulation, we propose an accelerated method that can be characterised as almost exact, in the sense that it can be arbitrarily precise. We discuss how it is possible to sample from the trajectory space rather than the transition space. This approach requires fewer random samples than a typical simulation algorithm. Most importantly, our approach does not rely on particular assumptions with respect to the model properties, in contrast to otherwise more efficient approaches.

published_or_final_version / abstract / toc / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Mathematics / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

No description available.

No description available.

No description available.