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The estimation of three-dimensional position information from two-dimensional images in computer vision systems can be formulated as a state estimation problem for a nonlinear perspective dynamic system. The multi-output state estimation problem has been treated by several authors using methods for nonlinear observer design. This paper shows that a perspective system can be transformed to two obse
This thesis concerns ego-motion and pose estimation of a single camera under the assumptions of planar motion and constant internal camera parameters. Planar motion is common for cameras mounted onto mobile robots, particularly in indoor scenarios, as they remain at a constant height above the ground plane. In Paper A, a parametrisation of the camera motion and pose is presented, along with an ite
Is the real problem in finding the relative orientation of two viewpoints the correspondence problem? We argue that this is only one difficulty. Even with known correspondences, popular methods like the eight point algorithm and minimal solvers may break down due to planar scenes or small relative motions. In this paper, we derive a simple, brute-force algorithm which is both robust to outliers an
This paper is concerned with model fitting in the presence of noise and outliers. Previously it has been shown that the number of outliers can be minimized with polynomial complexity in the number of measurements. This paper improves on these results in two ways. First, it is shown that for a large class of problems, the statistically more desirable truncated L-2-norm can be optimized with the sam
In this paper, we develop a system for microphone selflocalization based on ambient sound, without any assumptions on the 3D locations of the microphones and sound sources. We aim at developing a system capable of dealing with multiple moving sound sources. We will show that this is possible given that there are instances where there are only one dominating sound source. In the first step of the s
Segmentation is an important task in all kinds of image analysis. In medical image analysis segmentation has a great clinical value since the aim could be to localize organs or pathologies in order to raise the quality of diagnoses. This thesis consists of three papers where different segmentation techniques have been applied to different imaging modalities. In the first paper a full computer assi
Patients waiting for heart transplantation due to a failing heart can get a left ventricular assist device (LVAD) implanted through open chest surgery. The device consists of a pump that pumps blood from the left ventricle into the aorta. To get the correct rotation speed of the pump, the physicians consider a number of measurements as well as a sequence of echocardiographic images. The important
This paper describes a method of finding thin, elongated structures in images and volumes. We use shortest paths to minimize very general functionals of higher-order curve properties, such as curvature and torsion. Our globally optimal method uses line graphs and its runtime is polynomial in the size of the discretization, often in the order of seconds on a single computer. To our knowledge, we ar
Abstract in UndeterminedAccessibility to adequate health services is a basic human right. Israeli road blocks and checkpoints inhibit access to health care for the Palestinian population. While other studies have dealt with the impact of the barriers, few are based on actual measurements of transport times between locations. Geographical information systems (GIS) and network analysis were used to
Abstract in Undetermined Syria; Islam and state
Cellular signalling and potential agents to combat prostate cancer Prostate cancer (PCa) arises as a result of uncontrolled proliferation of the cells in the male prostate gland. The male androgenic hormone, testosterone fuels prostate cancer cells. In the cells of the prostate, the androgen receptor (AR) acts as the signal receiver and effector of androgen, ultimately leading to the growth and s
Fast algorithms for unequally spaced discrete Laplace transforms are presented. The algorithms are approximate up to a prescribed choice of computational precision, and they employ modified versions of algorithms for unequally spaced fast Fourier transforms using Gaussians. Various configurations of sums with equally and unequally spaced points can be dealt with. In contrast to previously presente
The diurnal cycle of precipitation is a fundamental mode of climatic variability. Based on an hourly 0.1° × 0.1° gauge-satellite merged precipitation dataset, which is provided by the China Meteorological Data Service Center, this study used the K-means clustering algorithm to map the diurnal cycles of precipitation in summer over China. The obtained maps can objectively delineate irregular region
A fully automatic system for abdominal organ segmentation is presented. As a first step, an organ localization is obtained via a robust and efficient feature registration method where the center of the organ is estimated together with a region of interest surrounding the center. Then, a convolutional neural network performing voxelwise classification is applied. Two convolutional neural networks o
CONTEXT: Viral/bacterial infection is proposed as a trigger for the autoimmune thyroid diseases (AITD): Graves' disease (GD) and Hashimoto's thyroiditis (HT). Previous studies in European Caucasian AITD subjects found higher birth rates in the autumn/winter, suggesting those born in the autumn/winter experience increased viral/bacterial exposure after birth, impacting upon immune system developmen
In this country report, as well as the accompanying general report, the authors revisit a 1999 European Taxation article discussing tax avoidance, which stemmed from the first European Association of Tax Law Professors meeting. The authors explore shifts in tax avoidance perception due to the introduction of the treaty general anti-avoidance rule (GAAR) (principal purpose test), EU GAAR and specif
This article probes the writings of the Jewish Trotskyist thinker Daniel Bensaïd (1946–2010) in light of recent debates on political theology. In contrast to what is sometimes explicitly referred to as ‘apocalyptic political theology’, it makes a case for what may be described as a ‘prophetic political theology’. Yet it is not obvious to claim Bensaïd as a proponent for such a project, since he ex
The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we
