Matrix Capsule Networks with EM Routing, introduced by Hinton et al. (2018a), offer a powerful way to model part-whole relationships and pose information in neural networks. However, their broader adoption has been limited, possibly due in part to the complexity of the model and inconsistencies between the original paper and its official implementation. In this thesis, we present a faithful and modular reimplementation of the Matrix Capsule Network in TensorFlow 2, addressing missing and unclear aspects of the original description. Our implementation achieves competitive results on the SmallNORB dataset and is designed to be accessible, adaptable, and well-documented. By clarifying key architectural details and providing open-source code, we aim to support further research and lower the barrier to working with capsule-based models.

This paper presents a method which is capable of creating an object centered world description based upon consecutive measurements about an unknown number of static objects using Bayesian inference. The objects are represented by a two dimensional position, with the aim of adding more attributes in future works. This objective is reformulated into a clustering problem which is then solved using a structure learning method. It is implemented using RxInfer which uses the message passing algorithm in combination with factor graphs to perform Bayesian inference. The results indicate a promising performance of the structure learning model, but also show signs that the object representation has been over simplified. The future works section provides guidance on how the model complexity can be increased by adding additional attributes in order to improve performance.

To ensure the safety of a spacecraft, operators collect thousands of telemetry signals and monitor them for anomalies, which is both expensive and time-consuming. Space agencies have been researching Machine Learning (ML)-based Time Series Anomaly Detection (TSAD) methods to improve automation, but this is hindered by a lack of high-quality benchmarks. This thesis explores the application of ML-based TSAD on instrument telemetry data for the XMM-Newton space telescope. The data was explored to find several unique challenges, such as recurring eclipses and a high volume of missing data. A methodology was then developed to pre-process the raw data into an ML-compatible format, detect anomalies using a semi-supervised forecasting approach and post-process the detections into a benchmark-suitable format. The method yielded over 40 detections, which were partially validated in discussion with instrument engineers. A refined methodology, incorporating areas for improvement, is presented to proceed with an eventual XMM-Newton anomaly benchmark.

Humans are our best example of the ability to learn a structure of the world through observation of environmental regularities. Specifically, humans can learn about different objects, different classes of objects, and different class-specific behaviors. Fundamental to these human abilities is evolved sensory hardware and automatic pattern recognition systems thought to be powered in part by the leading neuroscience theory of predictive coding. Artificial intelligence research is often inspired by neuroscience and algorithms already exist that implement predictive coding. In this paper, we seek to evaluate a leading predictive coding video-prediction algorithm, PredNet, for its ability to perform human-like learning of the types mentioned. By successfully training PredNet on a custom Simple Shape Motion (SSM) video dataset that explicitly requires structure learning to occur in order to accurately predict the next frame, we establish that PredNet is capable of rudimentary structure learning. We investigate PredNet filters and feature maps but find scant evidence of truly symbolic knowledge, and propose instead that PredNet performs semi-symbolic learning. We perform ablation studies that reveal the aspects of PredNet that critically contribute to its structure learning ability. Finally, we detail a set of modifications made to PredNet to allow object-centric processing as a promising step change towards human-like structure learning. Evaluation results and investigations are provided. Performance was slightly worse than Baseline, likely due to a noted implementation flaw. Code and instructions to reproduce dataset creation and model training / evaluation are available at https://github.com/ofSingularMind/parallel_prednet.

This thesis presents an approach to monocular depth estimation for Unmanned Aerial Vehicles (UAVs). Monocular depth estimation is a critical perception task for UAVs, enabling them to infer depth information from visual data without relying on heavy or power-consuming sensors such as LiDAR or stereo cameras. Given the operational constraints of UAVs, such as limited payload and energy resources, robust and efficient depth estimation methods are required to facilitate safe navigation and environmental interaction. The proposed methodology in this thesis integrates visual data from a monocular camera with inertial measurements from an Inertial Measurement Unit (IMU) sensor. This combination aims to address challenges such as scale ambiguity in the depth estimates and inaccuracies in dynamic environments that are common in aerial operations. The integration of IMU data with a differentiable camera-centric Extended Kalman Filter (EKF) allows for better ego-motion estimation, effectively calibrating the visual information with drone dynamics. The method further incorporates depth map frame prediction, leveraging initial depth estimates along with temporal dynamics to predict future depth maps. This predictive capability improves efficiency by reducing the need for full depth estimation in every frame, allowing robotic agents to anticipate environmental changes. The evaluation on simulated and real-world datasets shows that while the algorithm performs well over short forecast horizons, accumulating errors from IMU data and the assumption of a static environment limit its long-term accuracy. The future depth map prediction algorithm reduced the need for DynaDepth from 10 runs per second to 2, and on the Mid-Air dataset, from 25 to 5. Additionally, this study provides a foundation for future work, including the integration of an object-oriented frame prediction algorithm.

Quality control is considered an important process in manufacturing to minimise waste related to the manufacturing process. A way of performing quality control is with help of machine vision. Understanding all decisions that are to be made when designing a machine vision system for quality control is essential, but challenging. This research presents a method that aids decision-makers in the process of designing these machine vision systems for quality control so the efficiency of the decision-making process can be increased. The method results in a knowledge base and a tool that can both be considered as useful to decision-makers. The method was designed by following a Design Science Research approach and the method was applied on a use case in the automotive industry to prove that it addresses its goal.