Recent additions

  • Towards population counting of marine mammals based on drone images 

    Røkenes, Sigurd (Mastergradsoppgave; Master thesis, 2022-07-11)
    In marine science, there is a need for tools for population counting of species. Through this thesis we aim to achieve the follow three objectives: first, briefly discuss the state-of-the-art object detectors that can be used for the detection of porpoises in drone images/videos. Second, test and compare a few stateof-the-art object detectors in both quantitative and qualitative manner. Third, based ...
  • Deep Representation-aligned Graph Multi-view Clustering for Limited Labeled Multi-modal Health Data 

    Grimstad, Erland (Mastergradsoppgave; Master thesis, 2022-06-01)
    Today, many fields are characterised by having extensive quantities of data from a wide range of dissimilar sources and domains. One such field is medicine, in which data contain exhaustive combinations of spatial, temporal, linear, and relational data. Often lacking expert-assessed labels, much of this data would require analysis within the fields of unsupervised or semi-supervised learning. Thus, ...
  • Inference Guided Few-Shot Segmentation 

    Burman, Joel (Master thesis; Mastergradsoppgave, 2022-06-22)
    Few-shot segmentation has in recent years gotten a lot of attention. The reason is its ability to segment images from classes based on only a handful of labeled support images. This opens up many possibilities when the need for a big dataset is removed. To do this a few-shot segmentation network need to extract as much quality information from each support image as possible. In this thesis we ...
  • Machine Learning for Classifying Marine Vegetation from Hyperspectral Drone Data in the Norwegian coast 

    Grue, Silje B.S. (Master thesis; Mastergradsoppgave, 2022-05-30)
    Along the Norwegian coasts the presence of blue forests are the key marine habitats. Due to increased anthropogenic activity and climate change, the health and extent of the blue forests is threatened. However, no low-cost, reliable system for monitoring blue forests exists in Norway at this time. This thesis studied machine learning methods to classify marine vegetation from hyperspectral data ...
  • Validating Uncertainty-Aware Virtual Sensors For Industry 4.0 

    Mohammad, Gutama Ibrahim (Mastergradsoppgave; Master thesis, 2022-01-26)
    In industry 4.0 manufacturing, sensors provide information about the state, behavior, and performance of processes. Therefore, one of the main goals of Industry 4.0 is to collect high-quality data to realize its business goal, namely zero-defect manufacturing, and high-quality products. However, hardware sensors cannot always gather quality data due to several factors. First, industrial 4.0 deploys ...
  • ConvMixerSeg: Weakly Supervised Semantic Segmentation for CT Liver Images 

    Joakimsen, Harald Lykke (Mastergradsoppgave; Master thesis, 2021-12-17)
    The predictive power of modern deep learning approaches is posed to revolutionize the medical imaging field, however, their usefulness and applicability are severely limited by the lack of well annotated data. Liver segmentation in CT images is an application that could benefit particularly well from less data hungry methods and potentially lead to better liver volume estimation and tumor detection. ...
  • Imputation and classification of time series with missing data using machine learning 

    Dretvik, Vilde Fonn (Mastergradsoppgave; Master thesis, 2021-06-21)
    This work is about classifying time series with missing data with the help of imputation and selected machine learning algorithms and methods. The author has used imputation to replace missing values in two data sets, one containing surgical site infection (SSI) data of 11 types of blood samples of patients over 20 days, and another data set called uwave which contain 3D accelerometer data of several ...
  • Investigating the Impact of Susceptibility Artifacts on Adjacent Tumors in PET/MRI through Simulated Tomography Experiments 

    Olsen, Erlend Bredal (Mastergradsoppgave; Master thesis, 2021-06-01)
    For quantitative PET imaging, attenuation correction (AC) is mandatory. Currently, all main vendors of hybrid PET/MRI systems apply a segmentation-based approach to compute a Dixon AC-map based on fat and water images derived from in- and opposed-phase MR-images. Changes in magnetic susceptibility pose major problems for MRI, which may lead to artifacts resulting in tissue misclassification in the ...
  • Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization 

    Nilsen, Torjus (Mastergradsoppgave; Master thesis, 2021-06-01)
    Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in the Arctic. A current challenge however is that most sensory equipment can only capture one or fewer ...
  • MIR-based in-situ measurement of Silicon crystal-melt interface 

    Jensen, Mathias N. (Master thesis; Mastergradsoppgave, 2020-06-29)
    The project explores the a proposed MIR-based measurement system for measuring the deflection of the interface between the crystal and melt during production of mono-crystalline Silicon in the Czochralski process. The absorption spectrum is modeled and the specific absorption for a select set of wavelengths is estimated for temperatures approching 1687K. It was estimated that the intrinsic absorption ...
  • Wideband Self-Interference Cancellation Using Multi-Tap Filter in Radar Front End 

    Heiskel, Bendik (Mastergradsoppgave; Master thesis, 2020-12-14)
    The largest hurdle in full duplex wireless systems is the self-interference introduced by the transmitted signal into the received signal. In multi antenna systems this interference is caused by the direct coupling between the transmitting and receiving antennas. In systems where the transmitter and receiver uses the same antenna the interference is caused by inadequate isolation between the ...
  • Introducing Soft Option-Critic for Blood Glucose Control in Type 1 Diabetes : Exploiting Abstraction of Actions for Automated Insulin Administration 

    Jenssen, Christian (Master thesis; Mastergradsoppgave, 2020-07-15)
    Type 1 Diabetes (T1D) is an autoimmune disease where the insulin-producing cells are damaged and unable to produce sufficient amounts of insulin, causing an inability to regulate the body's blood sugar levels. Administrating insulin is necessary for blood glucose regulation, requiring diligent and continuous care from the patient to avoid critical health risks. The dynamics governing insulin-glucose ...
  • Numerical and experimental investigation of absorbing polymer films suitable for boundary photoacoustic imaging 

    Salmi, Marte Helene Skogdahl (Master thesis; Mastergradsoppgave, 2020-07-13)
    One of the main challenges in conventional photoacoustic methods, is that thin biological samples typically have low optical absorption in the visible region. Therefore, it is often necessary to stain or label the sample with a color which provide sufficient absorption for the laser wavelength used in the scanning system. Unfortunately, the labeling often introduce unwanted properties to the biological ...
  • Deep Learning of Oriented Bounding Box Regression Networks for Ship Detection in Optical Satellite Images 

    Sandland, Åsmund Mikael (Master thesis; Mastergradsoppgave, 2020-05-31)
    Maritime surveillance is important for management of maritime traffic and to prevent activities like illegal fishing, hazardous cargo transportation, piracy, and smuggling of goods and humans. Remote sensing is frequently used for positioning vessels that are not transmitting via the Automatic Identification System (AIS). Modern optical remote sensing instruments provide high-resolutional imagery, ...
  • Comparison of the Ice Watch Database and Sea Ice Classification from Sentinel-1 Imagery 

    Pedersen, Joakim Lillehaug (Master thesis; Mastergradsoppgave, 2019-12-13)
    In this thesis,we investigate the potential use of in-situ sea ice observations from the Ice Watch database as ground truth data for an automated classification algorithm of sea ice types from Sentinel-1 SAR data. The Ice Watch database and the Sentinel-1 data archive are searched for in-situ observations and satellite data acquisitions in Extra Wide swath mode overlapping in both space and time. ...
  • Affinity-Guided Image-to-Image Translation for Unsupervised Heterogeneous Change Detection 

    Hansen, Mads Adrian (Master thesis; Mastergradsoppgave, 2019-12-16)
    Change detection in earth observation remote sensing images can be used to describe the extent of natural disasters, e.g., forest fires and floods. When time is of the essence, the ability to utilize heterogeneous images is fundamental, i.e., images that are not directly comparable due to the sensors used or the capturing conditions. The recent advances in machine learning have dispersed into the ...
  • Investigating the effect of class-imbalance on convolutional neural networks for angiodysplasia detection 

    Kaspersen, Oskar Anstein (Master thesis; Mastergradsoppgave, 2019-10-31)
    In this thesis, we look at a deep learning approach to AD detection and focus specifically on the problem of class imbalance, which arises from the fact that lesions only occupy a small part of the images, by analyzing how weighting of the loss function can help address this issue. Balancing the weights of the foreground and background class in the cost-function was found to be crucial to achieve ...
  • Segmentation and Unsupervised Adversarial Domain Adaptation Between Medical Imaging Modalities 

    Strauman, Andreas Storvik (Master thesis; Mastergradsoppgave, 2019-07-13)
    Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives. Furthermore, creating methods that generically, accurately and successfully process unlabelled ...
  • An investigation of the robustness of distance measure-based supervised labelling of segmented remote sensing images 

    Kiærbech, Åshild (Master thesis; Mastergradsoppgave, 2019-06-03)
    Unsupervised clustering methods on remote sensing images have shown good results. However, this type of machine learning needs additional labelling to be an end-to-end classification in the same manner as traditional supervised classification. The automation of the labelling needs further exploration. We want to investigate the robustness of a supervised automatic labelling scheme by comparing a ...
  • Correction Strategies for Breathing Induced Motion in PET Images 

    Bakkevoll, Stian (Master thesis; Mastergradsoppgave, 2019-06-03)
    Breathing induced motion highly impacts the quality of PET images. Motion blur impacts important parameters such as standardised uptake value (SUV) and lesion volume. To be able to stage lung cancer correctly, and provide correct treatment, the impact of motion needs to be reduced. There exists techniques to combat the breathing motion, such as elastic motion correction and gating strategies. ...

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