Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections... Show moreSewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed. Instead of repairing sewer pipes when a problem becomes critical, such inspections allow municipalities to plan maintenance.Sewer pipe inspections are an attractive target for automation. While a potential improvement in terms of assessment quality and processing efficiency is generally promised by automation, in this case we would also decrease the variability which is a current problem. Besides the reasons for automating, the methods for automating are also attractive: a lot of (visual) data has been gathered over the past decades which may be used to train algorithms.This thesis compiles the results of five years of research into the possible automation of sewer pipe inspections with the tools of machine learning and computer vision. In this thesis, three distinct, yet complementary approaches to automating sewer pipe inspections are described:- Image-Based Unsupervised Anomaly Detection- Convolutional Neural Network Classification- Stereovision and Geometry Reconstruction Show less
A convolutional neural network (CNN) is a biologically inspired algorithm, highly capable at processing images and videos. Nowadays, CNNs are widely known and used: they watch our safety from the... Show moreA convolutional neural network (CNN) is a biologically inspired algorithm, highly capable at processing images and videos. Nowadays, CNNs are widely known and used: they watch our safety from the CCTV cameras, help doctors diagnose diseases, navigate cars, and do many other important things. One of the recent trends is to execute CNNs on edge devices: cameras, mobile phones, smart watches, etc. This helps to run CNNs faster and ensures privacy of the data used by the CNNs. This, however, is difficult to do. The problem is that the edge devices are small and often do not have enough resources to execute CNNs. In my dissertation, I study this problem and offer solutions for it. I propose specific manners to design and execute CNNs, so that they can run on edge devices efficiently. Show less
Tomography is a powerful technique to non-destructively determine the interior structure of an object.Usually, a series of projection images (e.g.\ X-ray images) is acquired from a range of... Show moreTomography is a powerful technique to non-destructively determine the interior structure of an object.Usually, a series of projection images (e.g.\ X-ray images) is acquired from a range of different positions.from these projection images, a reconstruction of the object's interior is computed. Many advanced applications require fast acquisition, effectively limiting the number of projection images and imposing a level of noise on these images. These limitations result in artifacts (deficiencies) in the reconstructed images. Recently, deep neural networks have emerged as a powerful technique to remove these limited-data artifacts from reconstructed images, often outperformingconventional state-of-the-art techniques. To perform this task, the networks are typically trained on a dataset of paired low-quality and high-quality images of similar objects. This is a major obstacle to their use in many practical applications. In this thesis, we explore techniques to employ deep learning in advanced experiments where measuring additional objects is not possible. Show less
This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.For cross-modal retrieval, Shannon... Show moreThis thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.For cross-modal retrieval, Shannon information entropy and adversarial learning are integrated to learn a common latent space for image data and text data. Furthermore, this thesis explores single-modal image retrieval in an incremental learning context to reduce the catastrophic forgetting of deep models, thereby expanding the continuous retrieval ability. The efficacy of the proposed methods in this thesis is verified by thorough experiments on the considered datasets. This thesis also gives an overview of new ideas and trends for multimodal content understanding. Show less
In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional... Show moreIn this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations. CFD simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains and they are frequently used for studying the effects of flow pat-terns and design choices on many engineering designs, such as wing, car and engineshapes. Due to the high dimensional aspect of the data, it is difficult to model to-ward achieving critical goals such as optimizing lift and drag forces. The key research question addressed in this thesis is whether we develop automated approaches that accurately abstract this information? We tackle these issues by studying a closely re-lated field, 3D computer vision, and adapt approaches to the particular data type.Moreover, inspired by this data type we propose new, deep learning, approaches that are also applied to traditional computer vision. Show less
