Building models from multiple point sets with kernel density estimation

One of the fundamental problems in computer vision is point set registration. Point set registration finds use in many important applications and in particular can be considered one of the crucial stages involved in the reconstruction of models of physical objects and environments from depth sensor data. The problem of globally aligning multiple point sets, representing spatial shape measurements from varying sensor viewpoints, into a common frame of reference is a complex task that is imperative due to the large number of critical functions that accurate and reliable model reconstructions contribute to. In this thesis we focus on improving the quality and feasibility of model and environment reconstruction through the enhancement of multi-view point set registration techniques. The thesis makes the following contributions: First, we demonstrate that employing kernel density estimation to reason about the unknown generating surfaces that range sensors measure allows us to express measurement variability, uncertainty and also to separate the problems of model design and viewpoint alignment optimisation. Our surface estimates define novel view alignment objective functions that inform the registration process. Our surfaces can be estimated from point clouds in a datadriven fashion. Through experiments on a variety of datasets we demonstrate that we have developed a novel and effective solution to the simultaneous multi-view registration problem. We then focus on constructing a distributed computation framework capable of solving generic high-throughput computational problems. We present a novel task-farming model that we call Semi-Synchronised Task Farming (SSTF), capable of modelling and subsequently solving computationally distributable problems that benefit from both independent and dependent distributed components and a level of communication between process elements. We demonstrate that this framework is a novel schema for parallel computer vision algorithms and evaluate the performance to establish computational gains over serial implementations. We couple this framework with an accurate computation-time prediction model to contribute a novel structure appropriate for addressing expensive real-world algorithms with substantial parallel performance and predictable time savings. Finally, we focus on a timely instance of the multi-view registration problem: modern range sensors provide large numbers of viewpoint samples that result in an abundance of depth data information. The ability to utilise this abundance of depth data in a feasible and principled fashion is of importance to many emerging application areas making use of spatial information. We develop novel methodology for the registration of depth measurements acquired from many viewpoints capturing physical object surfaces. By defining registration and alignment quality metrics based on our density estimation framework we construct an optimisation methodology that implicitly considers all viewpoints simultaneously. We use a non-parametric data-driven approach to consider varying object complexity and guide large view-set spatial transform optimisations. By aligning large numbers of partial, arbitrary-pose views we evaluate this strategy quantitatively on large view-set range sensor data where we find that we can improve registration accuracy over existing methods and contribute increased registration robustness to the magnitude of coarse seed alignment. This allows large-scale registration on problem instances exhibiting varying object complexity with the added advantage of massive parallel efficiency

The Limits of the European Union’s Transformative Power: Pathologies of Europeanization and Rule of Law Reform in Central and Eastern Europe

This thesis examines the impact of the European Union (EU) on the development of the rule of law in Central and Eastern Europe. The topic is addressed through a mixed methods study which consists of a quantitative comparative analysis of three country groups from Central and Eastern Europe (1. Central Europe and the Baltics, CEB; 2. South Eastern Europe, SEE; 3. Commonwealth of Independent States, CIS) and three qualitative case studies on Poland, Romania and Moldova. The empirical analysis is based on an innovative set of indicators and revealing insights from numerous qualitative interviews. The findings of the study suggest that the impact of the EU is differential, both healthy and pathological. While EU-driven judicial reforms increase judicial capacity and align domestic legislation with European and international standards (substantive legality), they do not improve and even lead to a deterioration of judicial impartiality and formal legality, resulting in several reform pathologies, such as instable, incoherent and non-enforced laws and in more politicized and incoherent judicial systems, which undermine the development of the rule of law. These pathological effects occur mostly in weak rule of law countries from SEE (Romania) and CIS (Moldova), in contrast to more healthy effects in advanced, strong rule of law countries from CEB (Poland). The dissimilar development in the rule of law across countries is explained in relation to the conduct of reforms. Successful reformers like Poland, which consolidate the rule of law, have strong and independent horizontal accountability institutions (e.g. Constitutional Court, Ombudsman, judiciary), which mitigate or alleviate reform pathologies and ensure that reforms are conducted in an accountable, gradual and non-politicized way. Unsuccessful reformers, like Romania and Moldova, lack these independent checks on reformers and thus fail to establish the rule of law. Based on the findings from the case studies an original typology of healthy and pathological reform paths is proposed, which draws on the logic of circular and cumulative causation and emphasizes the mutual reinforcement between domestic conditions and the reform approach of transnational coalitions. The proposed typology implies that EU conditionality is not transformative, but rather reinforces existing healthy and pathological reform paths, thus cementing the existing divergence in the rule of law across post-communist countries. This thesis further makes several policy recommendations to remedy the pathological impact of donor-driven reforms

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset