Machine learning generally requires large amounts of data, however data is often limited. On the whole the amount of data needed grows with the complexity of the problem to be solved. Utilising transfer learning, data augmentation and problem reduction, acceptable performance can be achieved with limited data for a multitude of tasks. The goal of this master project is to develop an artificial neural network-based model for food waste analysis, an area in which large quantities of data is not yet readily available. Given two images an algorithm is expected to identify what has changed in the image, ignore the uncharged areas even though they might contain objects which can be classified and finally classify the change. The approach chosen in this project was to attempt to reduce the problem the machine learning algorithm has to solve by subtracting the images before they are handled by the neural network. In theory this should resolve both object localisation and filtering of uninteresting objects, which only leaves classification to the neural network. Such a procedure significantly simplifies the task to be resolved by the neural network, which results in reduced need for training data as well as keeping the process of gathering data relatively simple and fast. Several models were assessed and theories of adaptation of the neural network to this particular task were evaluated. Test accuracy of at best 78.9% was achieved with a limited dataset of about 1000 images with 10 different classes. This performance was accomplished by a siamese neural network based on VGG19 utilising triplet loss and training data using subtraction as a basis for ground truth mask creation, which was multiplied with the image containing the changed object. / Maskininlärning kräver generellt mycket data, men stora mängder data står inte alltid till förfogande. Generellt ökar behovet av data med problemets komplexitet. Med hjälp av överföringsinlärning, dataaugumentation och problemreduktion kan dock acceptabel prestanda erhållas på begränsad datamängd för flera uppgifter.  Målet med denna masteruppsats är att ta fram en modell baserad på artificiella neurala nätverk för matavfallsanalys, ett område inom vilket stora mängder data ännu inte finns tillgängligt. Givet två bilder väntas en algoritm identifiera vad som ändrats i bilden, ignorera de oförändrade områdena även om dessa innehåller objekt som kan klassificeras och slutligen klassificera ändringen. Tillvägagångssättet som valdes var att försöka reducera problemet som maskininlärningsalgoritmen, i detta fall ett artificiellt neuralt nätverk, behöver hantera genom att subtrahera bilderna innan de hanterades av det neurala nätverket. I teorin bör detta ta hand om både objektslokaliseringen och filtreringen av ointressanta objekt, vilket endast lämnar klassificeringen till det neurala nätverket. Ett sådant tillvägagångssätt förenklar problemet som det neurala nätverket behöver lösa avsevärt och resulterar i minskat behov av träningsdata, samtidigt som datainsamling hålls relativt snabbt och simpelt.  Flera olika modeller utvärderades och teorier om specialanpassningar av neurala nätverk för denna uppgift evaluerades. En testnoggrannhet på som bäst 78.9% uppnåddes med begränsad datamängd om ca 1000 bilder med 10 klasser. Denna prestation erhölls med ett siamesiskt neuralt nätverk baserat på VGG19 med tripletförlust och träningsdata som använde subtraktion av bilder som grund för framställning av grundsanningsmasker (eng. Ground truth masks) multiplicerade med bilden innehållande förändringen.

If Pulmonary Tuberculosis (PTB) is detected early in a patient, the greater the chances of treating and curing the disease. Early detection of PTB could result in an overall lower mortality rate. Detection of PTB is achieved in many ways, for instance, by using tests like the sputum culture test. The problem is that conducting tests like these can be a lengthy process and takes up precious time. The best and quickest PTB detection method is viewing the chest X-Ray image (CXR) of the patient. To make an accurate diagnosis requires a qualified professional Radiologist. Neural Networks have been around for several years but is only now making ground-breaking advancements in speech and image processing because of the increased processing power at our disposal. Artificial intelligence, especially Deep Learning Convolutional Neural Networks (DLCNN), has the potential to diagnose and detect the disease immediately. If DLCNN can be used in conjunction with the professional medical institutions, crucial time and effort can be saved. This project aims to determine and investigate proper methods to identify and detect Pulmonary Tuberculosis in the patient chest X-Ray images using DLCNN. Detection accuracy and success form a crucial part of the research. Simulations on an input dataset of infected and healthy patients are carried out. My research consists of firstly evaluating the colour depth and image resolution of the input images. The best resolution to use is found to be 64x64. Subsequently, a colour depth of 8 bit is found to be optimal for CXR images. Secondly, building upon the optimal resolution and colour depth, various image pre-processing techniques are evaluated. In further simulations, the pre-processed images with the best outcome are used. Thirdly the techniques evaluated are transfer learning, hyperparameter adjustment and data augmentation. Of these, the best results are obtained from data augmentation. Fourthly, a proposed hybrid approach. The hybrid method is a mixture of CAD and DLCNN using only the lung ROI images as training data. Finally, a combination of the proposed hybrid method, coupled with augmented data and specific hyperparameter adjustment, is evaluated. Overall, the best result is obtained from the proposed hybrid method combined with synthetic augmented data and specific hyperparameter adjustment. / Electrical and Mining Engineering

Due to hardware limitations, 3D sensors like LiDAR often produce sparse and noisy point clouds. Point cloud upsampling is the task of converting such point clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling using deep neural networks. The effectiveness of a point cloud upsampling neural network heavily relies on the upsampling module and the feature extractor used therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle. NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves the performance of previous upsampling methods. I also propose a new GCN-based multi-scale feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, Inception DenseGCN learns a hierarchical feature representation and enables further performance gains. I combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling network called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.

This thesis explores the current deep learning (DL) approaches to computer aided diagnosis (CAD) of digital mammographic images and presents two novel designs for overcoming current obstacles endemic to the field, using convolutional neural networks (CNNs). The first method employed utilizes Bayesian statistics to perform decision level fusion from multiple images of an individual. The second method utilizes a new data pre-processing scheme to artificially expand the limited available training data and reduce model overitting.

With the inception of music streaming and media content delivery platforms, there has been a tremendous increase in the music available on the internet and the metadata associated with it. In this study, we address the problem of violin artist identification, which tries to classify the performing artist based on the learned features. Even though numerous previous works studied the problem in detail and developed features and deep learning models that can be used, an interesting fact was that most studies focused on artist identification in western popular music and less on Indian classical music. For the same reason, there was no standardized dataset for this purpose. Hence, we curated a new dataset consisting of audio recordings from 6 renowned South Indian Carnatic violin artists. In this study, we explore the use of log-Mel-spectrogram feature and the embeddings generated by a pre-learned VGGish network on a Convolutional Neural Network and Convolutional Recurrent Neural Network Model. From the experiments, we observe that the Convolutional Recurrent Neural Network model trained using the log-Mel-spectrogram feature gave the optimal performance with a classification accuracy of 71.70%. / Med starten av plattformar för musikströmning och leverans av mediainnehåll har det skett en enorm ökning av musiken tillgänglig på internet och den metadata som är associerad med den. I denna studie tar vi upp problemet med fiolkonstnärsidentifikation, som försöker klassificera den utövande konstnären utifrån de inlärda dragen. Även om många tidigare verk studerade problemet i detalj och utvecklade funktioner och modeller för djupinlärning som kan användas, var ett intressant faktum att de flesta studier fokuserade på artistidentifiering i västerländsk populärmusik och mindre på indisk klassisk musik. Av samma anledning fanns det ingen standardiserad datauppsättning för detta ändamål. Därför kurerade vi en ny datauppsättning bestående av ljudinspelningar från 6 kända sydindiska karnatiska violinkonstnärer. I den här studien utforskar vi användningen av log-Melspektrogramfunktionen och inbäddningarna som genereras av ett förinlärt VGGishnätverk på ett Convolutional Neural Network och Convolutional Recurrent Neural Network Model. Från experimenten observerar vi att modellen Convolutional Recurrent Neural Network tränad med hjälp av log-Mel-spektrogramfunktionen gav optimal prestanda med en klassificeringsnoggrannhet på 71,70%.

Homography estimation is a fundamental task in many computer vision applications, but many techniques for estimation rely on complicated feature extraction pipelines. We extend research in direct homography estimation (i.e. without explicit feature extraction) by implementing a convolutional network capable of estimating homographies. Previous work in deep learning based homography estimation calculates homographies between pairs of images, whereas our network takes single image input and registers it to a reference view where no image data is available. The application of the work is registering frames from American football video to a top-down view of the field. Our model manages to register frames in a test set with an average corner error equivalent to less than 2 yards. / Homografiuppskattning är ett förkrav för många problem inom datorseende, men många tekniker för att uppskatta homografier bygger på komplicerade processer för att extrahera särdrag mellan bilderna. Vi bygger på tidigare forskning inom direkt homografiuppskattning (alltså, utan att explicit extrahera särdrag) genom att  implementera ett Convolutional Neural Network (CNN) kapabelt av att direkt uppskatta homografier. Arbetet tillämpas för att registrera bilder från video av amerikansk fotball till en referensvy av fotbollsplanen. Vår modell registrerar bildramer från ett testset till referensvyn med ett snittfel i bildens hörn ekvivalent med knappt 2 yards.

No / We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.

Wildfires burn millions of acres of land each year leading to the destruction of homes and wildland ecosystems while costing governments billions in funding. As climate change intensifies drought volatility across the Western United States, wildfires are likely to become increasingly severe. Wildfire risk assessment and hazard maps are currently employed by fire services, but can often be outdated. This paper introduces an image-based dataset using climate and wildfire data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The dataset consists of 32 climate and topographical layers captured across 0.1 deg by 0.1 deg tiled regions in California and Nevada between 2015 and 2020, associated with whether the region later saw a wildfire incident. We trained a convolutional neural network (CNN) with the generated dataset to predict whether a region will see a wildfire incident given the climate data of that region. Convolutional neural networks are able to find spatial patterns in their multi-dimensional inputs, providing an additional layer of inference when compared to logistic regression (LR) or artificial neural network (ANN) models. To further understand feature importance, we performed an ablation study, concluding that vegetation products, fire history, water content, and evapotranspiration products resulted in increases in model performance, while land information products did not. While the novel convolutional neural network model did not show a large improvement over previous models, it retained the highest holistic measures such as area under the curve and average precision, indicating it is still a strong competitor to existing models. This introduction of the convolutional neural network approach expands the wealth of knowledge for the prediction of wildfire incidents and proves the usefulness of the novel, image-based dataset.

Acceleration is a central aim of clinical and technical research in magnetic resonance imaging (MRI) today, with the potential to increase robustness, accessibility and patient comfort, reduce cost, and enable entirely new kinds of examinations. A key component in this endeavor is image reconstruction, as most modern approaches build on advanced signal and image processing. Here, deep learning (DL)-based methods have recently shown considerable potential, with numerous publications demonstrating benefits for MRI reconstruction. However, these methods often come at the cost of an increased risk for subtle yet critical errors. Therefore, the aim of this thesis is to advance DL-based MRI reconstruction, while ensuring high quality and fidelity with measured data. A network architecture specifically suited for this purpose is the variational network (VN). To investigate the benefits these can bring to non-Cartesian cardiac imaging, the first part presents an application of VNs, which were specifically adapted to the reconstruction of accelerated spiral acquisitions. The proposed method is compared to a segmented exam, a U-Net and a compressed sensing (CS) model using qualitative and quantitative measures. While the U-Net performed poorly, the VN as well as the CS reconstruction showed good output quality. In functional cardiac imaging, the proposed real-time method with VN reconstruction substantially accelerates examinations over the gold-standard, from over 10 to just 1 minute. Clinical parameters agreed on average. Generally in MRI reconstruction, the assessment of image quality is complex, in particular for modern non-linear methods. Therefore, advanced techniques for precise evaluation of quality were subsequently demonstrated. With two distinct methods, resolution and amplification or suppression of noise are quantified locally in each pixel of a reconstruction. Using these, local maps of resolution and noise in parallel imaging (GRAPPA), CS, U-Net and VN reconstructions were determined for MR images of the brain. In the tested images, GRAPPA delivers uniform and ideal resolution, but amplifies noise noticeably. The other methods adapt their behavior to image structure, where different levels of local blurring were observed at edges compared to homogeneous areas, and noise was suppressed except at edges. Overall, VNs were found to combine a number of advantageous properties, including a good trade-off between resolution and noise, fast reconstruction times, and high overall image quality and fidelity of the produced output. Therefore, this network architecture seems highly promising for MRI reconstruction. / Eine Beschleunigung des Bildgebungsprozesses ist heute ein wichtiges Ziel von klinischer und technischer Forschung in der Magnetresonanztomographie (MRT). Dadurch könnten Robustheit, Verfügbarkeit und Patientenkomfort erhöht, Kosten gesenkt und ganz neue Arten von Untersuchungen möglich gemacht werden. Da sich die meisten modernen Ansätze hierfür auf eine fortgeschrittene Signal- und Bildverarbeitung stützen, ist die Bildrekonstruktion ein zentraler Baustein. In diesem Bereich haben Deep Learning (DL)-basierte Methoden in der jüngeren Vergangenheit bemerkenswertes Potenzial gezeigt und eine Vielzahl an Publikationen konnte deren Nutzen in der MRT-Rekonstruktion feststellen. Allerdings besteht dabei das Risiko von subtilen und doch kritischen Fehlern. Daher ist das Ziel dieser Arbeit, die DL-basierte MRT-Rekonstruktion weiterzuentwickeln, während gleichzeitig hohe Bildqualität und Treue der erzeugten Bilder mit den gemessenen Daten gewährleistet wird. Eine Netzwerkarchitektur, die dafür besonders geeignet ist, ist das Variational Network (VN). Um den Nutzen dieser Netzwerke für nicht-kartesische Herzbildgebung zu untersuchen, beschreibt der erste Teil dieser Arbeit eine Anwendung von VNs, welche spezifisch für die Rekonstruktion von beschleunigten Akquisitionen mit spiralen Auslesetrajektorien angepasst wurden. Die vorgeschlagene Methode wird mit einer segmentierten Rekonstruktion, einem U-Net, und einem Compressed Sensing (CS)-Modell anhand von qualitativen und quantitativen Metriken verglichen. Während das U-Net schlecht abschneidet, zeigen die VN- und CS-Methoden eine gute Bildqualität. In der funktionalen Herzbildgebung beschleunigt die vorgeschlagene Echtzeit-Methode mit VN-Rekonstruktion die Aufnahme gegenüber dem Goldstandard wesentlich, von etwa zehn zu nur einer Minute. Klinische Parameter stimmen im Mittel überein. Die Bewertung von Bildqualität in der MRT-Rekonstruktion ist im Allgemeinen komplex, vor allem für moderne, nichtlineare Methoden. Daher wurden anschließend forgeschrittene Techniken zur präsizen Analyse von Bildqualität demonstriert. Mit zwei separaten Methoden wurde einerseits die Auflösung und andererseits die Verstärkung oder Unterdrückung von Rauschen in jedem Pixel eines untersuchten Bildes lokal quantifiziert. Damit wurden lokale Karten von Auflösung und Rauschen in Rekonstruktionen durch Parallele Bildgebung (GRAPPA), CS, U-Net und VN für MR-Aufnahmen des Gehirns berechnet. In den untersuchten Bildern zeigte GRAPPA gleichmäßig eine ideale Auflösung, aber merkliche Rauschverstärkung. Die anderen Methoden verhalten sich lokal unterschiedlich je nach Struktur des untersuchten Bildes. Die gemessene lokale Unschärfe unterschied sich an den Kanten gegenüber homogenen Bildbereichen, und Rauschen wurde überall außer an Kanten unterdrückt. Insgesamt wurde für VNs eine Kombination von verschiedenen günstigen Eigenschaften festgestellt, unter anderem ein guter Kompromiss zwischen Auflösung und Rauschen, schnelle Laufzeit, und hohe Qualität und Datentreue der erzeugten Bilder. Daher erscheint diese Netzwerkarchitektur als ein äußerst vielversprechender Ansatz für MRT-Rekonstruktion.

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