Proper maintenance and inspection of aircraft and their engines is important for society. These engine inspections are performed using borescopes of which the footage is manually analysed. Having the opportunity to reconstruct a 3D model of the rotors would ease the inspection and introduce the possibility to automate the process. Monocular SLAM systems are capable of reconstructing such models in real-time using a video of the rotors. However, SLAM is not tested in environments similar to the aircraft turbine. This study, therefore, assesses the performance of different SLAM approaches in this specific setting. The results show that 3D reconstruction of aircraft engines using direct SLAM has potential for damage assessment. Further research into damage assessment using SLAM is therefore viable.

3D modeling techniques can be used to automate processes such as damage assessment in aircraft engines. Aircraft engines often have shiny and non-textured surfaces, where these modeling techniques often have poor performance. This paper gives more insight into the performance of interest detection/matching algorithms on shiny and non-textured surfaces as found in aircraft engine borescope inspection videos. These algorithms are often used in 3D modeling techniques. Three interest point detection/matching algorithms are executed on different test videos, and various metrics are calculated for each algorithm. This paper is the first paper that compares both recent and traditional computer vision interest point detection/matching algorithms in these specific settings, and contributes to a better understanding of the usability of feature-based 3D reconstruction techniques. The results show that more recent neural network-based approaches outperform traditional approaches.

Generative adversarial networks (GANs) are a popular method for image generation, capable of state-of-the-art. Despite this popularity, the generation of fabric patterns remains somewhat unexplored. A possible reason for this could be that there is no publicly shared dataset large enough to train models. However, research into this topic could be relevant because designers can take up to multiple weeks for a single design, but a trained GAN could generate thousands of designs per day. Next to this fabric patterns have characteristics that make them different from the datasets commonly used right now, which means an investigation could yield new insights into the capabilities of GANs. This research investigated the performance of several GAN models on the task of fabric pattern generation and compared these to another popular method of image generation: variational autoencoders. By using open-source implementations of GAN models, and a dataset of fabric patterns provided by Vlisco, a Dutch fabric company, the performance of GANs has been evaluated. To protect the interests of Vlisco, this dataset cannot be shared. The results show clear promise in the capabilities of GANs to replicate these complex patterns, but these results are not yet of the same quality that GANs have been able to achieve in other domains. However, these results are an improvement from those of previous research. The results achieved by variational autoencoders are similar to those of GANs, but state-of-the-art GANs perform better. Due to limited time and resources, only low-resolution samples have been generated for evaluation. In the future, higher-resolution images could be generated which could yield additional insights into the current capabilities of GANs in this domain.

The goal of this research is to find out whether it is possible to use a specific type of machine learning algorithm to create new data that resembles data in a given dataset. The algorithm that is used is a variational autoencoder (VAE), this is a machine learning algorithm based around data compression and decompression. The dataset is a set of roughly 47,000 images of colorful fabric pattern designs created bya company called Vlisco. The working of a VAE is explained and implementations are discussed. Samples of generated images are shown and their quality is discussed.Eventually the results are compared to the results of a different research that focuses on the same problem but with a general adversarial network (GAN). If such algorithms work well they could potentially provide designers with new designs or inspiration quickly.