AWeD: Automatic Weapons Detection

The goal of this project is to design an integrated system that allows for fast and reliable processing of high quality video data and in doing so detect and react to the presence of a firearm or other weaponry when used in a threatening or dangerous manner. This is accomplished through the combined use of computer vision processing techniques implemented on an FPGA as well as a convolutional neural network trained to determine the presence of a threat

Pedestrian detection and tracking using stereo vision techniques

Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies

MRI Artefact Augmentation: Robust Deep Learning Systems and Automated Quality Control

Quality control (QC) of magnetic resonance imaging (MRI) is essential to establish whether a scan or dataset meets a required set of standards. In MRI, many potential artefacts must be identified so that problematic images can either be excluded or accounted for in further image processing or analysis. To date, the gold standard for the identification of these issues is visual inspection by experts. A primary source of MRI artefacts is caused by patient movement, which can affect clinical diagnosis and impact the accuracy of Deep Learning systems. In this thesis, I present a method to simulate motion artefacts from artefact-free images to augment convolutional neural networks (CNNs), increasing training appearance variability and robustness to motion artefacts. I show that models trained with artefact augmentation generalise better and are more robust to real-world artefacts, with negligible cost to performance on clean data. I argue that it is often better to optimise frameworks end-to-end with artefact augmentation rather than learning to retrospectively remove artefacts, thus enforcing robustness to artefacts at the feature level representation of the data. The labour-intensive and subjective nature of QC has increased interest in automated methods. To address this, I approach MRI quality estimation as the uncertainty in performing a downstream task, using probabilistic CNNs to predict segmentation uncertainty as a function of the input data. Extending this framework, I introduce a novel decoupled uncertainty model, enabling separate uncertainty predictions for different types of image degradation. Training with an extended k-space artefact augmentation pipeline, the model provides informative measures of uncertainty on problematic real-world scans classified by QC raters and enables sources of segmentation uncertainty to be identified. Suitable quality for algorithmic processing may differ from an image's perceptual quality. Exploring this, I pose MRI visual quality assessment as an image restoration task. Using Bayesian CNNs to recover clean images from noisy data, I show that the uncertainty indicates the possible recoverability of an image. A multi-task network combining uncertainty-aware artefact recovery with tissue segmentation highlights the distinction between visual and algorithmic quality, which has the impact that, depending on the downstream task, less data should be discarded for purely visual quality reasons

Intermediate and extreme mass-ratio inspirals — astrophysics, science applications and detection using LISA

Black hole binaries with extreme (gtrsim104:1) or intermediate (~102–104:1) mass ratios are among the most interesting gravitational wave sources that are expected to be detected by the proposed laser interferometer space antenna (LISA). These sources have the potential to tell us much about astrophysics, but are also of unique importance for testing aspects of the general theory of relativity in the strong field regime. Here we discuss these sources from the perspectives of astrophysics, data analysis and applications to testing general relativity, providing both a description of the current state of knowledge and an outline of some of the outstanding questions that still need to be addressed. This review grew out of discussions at a workshop in September 2006 hosted by the Albert Einstein Institute in Golm, Germany

Contributions for the automatic description of multimodal scenes

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200