This thesis investigates the compensation and minimisation of distortions in images. Various forms of non-linear lens distortions are modelled and removed. Projective linear distortions are further minimised to give the closest ideal projection from erroneous cameras. Traditional camera calibration treats lens distortion simultaneously with camera pose and lens scaling factors. This often leads...

One of the newest developments within radiotherapy is integration Magnetic Resonance (MR) scanners and Megavoltage X-rays from linear accelerators into new MR-Linacs. Some MR-Linacs are able to perform gated treatments based on continuously acquired 2D MR images taken during dose delivery, which has potential reduce margins required for tumor coverage. We have developed a dosimetry system that ...

We introduce and study the Lattice Distortion Problem (LDP). LDP asks how “similar” two lattices are. I.e., what is the minimal distortion of a linear bijection between the two lattices? LDP generalizes the Lattice Isomorphism Problem (the lattice analogue of Graph Isomorphism), which simply asks whether the minimal distortion is one. As our first contribution, we show that the distortion betwe...

The rapid increase of non-linear loads in the last three decades has caused electrical quantities such as voltages and currents in 3-phase distribution systems to become distorted waveforms. This paper reviews, explains and discusses some new concepts, definitions and new available theory in the unbalanced and distorted systems. The usefulness of the proposed practical definitions is investigat...

In this work, we study a distributed source coding problem with multiple encoders, a central decoder and a joint distortion criterion. The encoders observe correlated sources which they quantize and communicate to a central decoder through a rate constrained, noiseless channel. The objective is to minimize the distortion as measured by a joint distortion criterion that depends on the sources an...

We provide a simulation study based on the model in §2.1 and we simulate data from the NLTCS based on our model, with varying levels of distortion. The varying levels of distortion (0, 0.25%, 0.5%, 1%, 2%, 5%) associated with the simulated data are then run using our MCMC algorithm to assess how well we can match under “noisy data.” Figure 3 illustrates an approximate linear relationship with F...