Filetype
This paper studies the problem of determining the absolute pose of a perspective camera observing a scene through a known refractive plane, the flat boundary between transparent media with different refractive indices. Efficient minimal solvers are developed for the 2D, known orientation and known rotation axis cases, and near-minimal solvers for the general calibrated and unknown focal length cas
The most used pavement for paved roads in the world is asphalt. It is therefore important that the asphalt is as durable as possible to avoid expensive repairs of the roads. One important factor of the durability of the road is the adherence between the stones and the bitumen that holds the stones together. The affinity is tested by the so called rolling bottle test, where one put stones covered i
3D human pose and shape estimation plays a vital role in many computer vision applications. There are many deep learning based methods attempting to solve the problem only relying on single-view RGB images for training the network. However, since some public datasets are captured from multi-view cameras system, we propose a novel method to tackle the problem by putting optimization-based multi-vie
3D human body reconstruction from monocular images has wide applications in our life, such as movie, animation, Virtual/Augmented Reality, medical research and so on. Due to the high freedom of human body in real scene and the ambiguity of inferring 3D objects from 2D images, it is a challenging task to accurately recover 3D human body models from images. In this thesis, we explore the methods for
A wide range of medical examinations are using analysis of images from different types of equipment. Using artificial intelligence, the assessments could be done automatically. This can have multiple benefits for the healthcare; reduce workload for medical doctors, decrease variations in diagnoses and cut waiting times for the patient as well as improve the performance. The aim of this thesis has
Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach. However, it is not clear how the set
The scan-on-receive (SCORE) is a key digital beamforming (DBF) technique for future high-resolution wide-swath synthetic aperture radar (SAR) systems. A sharp and high gain receive beam, steered in real time towards the expected direction of arrival (DoA) of the backscattering signal, allows for improved SAR imaging performance with respect to a conventional approach. Nevertheless, it also exposes
Molecular recognition is fundamental to biological signaling. A central question is how individual interactions between molecular moieties affect the thermodynamics of ligand binding to proteins and how these effects might propagate beyond the immediate neighborhood of the binding site. Here, we investigate this question by introducing minor changes in ligand structure and characterizing the effec
X-ray propagation-based imaging techniques are well established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase-retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and diffusion of such techniques difficult. Here, PyPhase, a free and open-source p
Prior results on target localization and circumnavigation with bearing measurements in $\mathbb {R}^{2}$ are extended with integral action, resulting in a control system that is robust to bounded load disturbances on the control inputs. Such disturbances may arise in practice due to modeling errors and need to be considered to ensure small tracking errors. The control inputs are modeled as the sys
This work considers direction-finding using a monostatic multiple-input multiple-output (MIMO) radar in the presence of impulsive noise. Employing a novel low-order covariance-based exponential kernel function, the proposed maximum likelihood (ML) formulation exploits an introduced quantum whale optimization algorithm (QWOA) to form the direction estimates. The resulting estimates are shown to be
Extra charges in semiconductor nanocrystals are of paramount importance for their electrically driven optoelectronic and photovoltaic applications. Optical excitations of such charged nanocrystals lead to rapid recombinationviaan Auger process, which can deteriorate the performance of the corresponding devices. While numerous articles report trion Auger processes in negatively charged nanocrystals
Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states d
Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima. Despite recent progress, the reason for this outcome is not fully understood. Could deep networks have very few, if
Purpose: The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [18F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA P
In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manne
Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques, known as Time-Difference-Of-Arrival (TDOA) techniques. Despite previous work, TDOA self-calibration i
Recurrent pregnancy loss (RPL), defined as three or more consecutive miscarriages, is hypothesized to share some of the same pathogenic factors as placenta-associated disorders. It has been hypothesized that a defect implantation causes pregnancy loss, while a partially impaired implantation may lead to late pregnancy complications. The aim of this retrospective register-based cohort study was to
