Blar i Institutt for datateknologi og informatikk på tittel
Viser treff 1545-1564 av 6615
-
Deep Generative Models for Realistic Image Anonymization
(Doctoral theses at NTNU;2023:421, Doctoral thesis, 2023)The following pages explore the use of generative models for realistic image anonymization. In summary, this thesis aims to address two primary objectives. First, develop generative models for synthesizing human figures ... -
Deep HDR Fusion: Deep Learning-based HDR Fusion of RAW images
(Master thesis, 2023)Capturing scenes with a wide dynamic range presents inherent challenges due to sensor limitations, resulting in a loss of dynamic range in captured images. By fusing multiple images captured at varying exposures, a single ... -
Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution
(Chapter, 2019)Deep learning algorithms have demonstrated state-of-the-art performance in various tasks of image restoration. This was made possible through the ability of CNNs to learn from large exemplar sets. However, the latter becomes ... -
Deep Learning Applied to Automatic Anomaly Detection in Capsule Video Endoscopy
(Master thesis, 2018)Introduction: Regularly screening of the gastrointestinal tract for polyps is the an important measure for preventing colorectal cancer. Screening large population's gastrointestinal tract is with todays common methods too ... -
A deep learning approach for brain tumor classification using MRI images
(Journal article; Peer reviewed, 2022) -
Deep Learning Approaches for Whiteboard Image Quality Enhancement
(Peer reviewed; Journal article, 2019)Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image ... -
Deep Learning based frameworks for 3D registration of differential and multimodal data
(Doctoral theses at NTNU;2023:153, Doctoral thesis, 2023)In recent decades, Visual Computing methodologies such as image processing and computer vision have addressed problems in the field of Cultural Heritage (CH)resulting in significant benefits. Specifically, accurate ... -
Deep Learning Controlled Temporal Upsampling - An Efficient Recurrent Convolutional Neural Network Controlled Architecture for Temporal Upsampling
(Master thesis, 2021)Rendering i sanntid blir stadig dyrere på grunn av skjermer med høyere oppløsning, høyere bildefrekvens og fotorealistisk grafikk. Kostnaden av rendering kan reduseres ved å rendere ved en lavere oppløsning enn skjermen, ... -
Deep Learning Enhanced Human Activity Recognition for Internet of Healthcare Things
(Peer reviewed; Journal article, 2020)Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied ... -
Deep Learning for Blind Calibration of Wireless Sensor Networks
(Master thesis, 2020)Temporal drift of low-cost sensors is a crucial problem when considering the applicability of wireless sensor networks (WSN). Since they provide highly local measurements, which is key to combat the ever increasing problem ... -
Deep Learning for Fault Prediction in Offshore Wind Turbines
(Master thesis, 2019)Denne oppgaven beskriver et feilprediksjonssystem for offshore vindturbiner basert på SCADA data. Hensikten med systemet er å gi tidlig advarsel for kommende feil. Dette vil kunne bidra til reduserte vedlikeholdskostnader ... -
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
(Peer reviewed; Journal article, 2022)Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using ... -
Deep Learning for Improved Diagnosis of Pathologies in Wireless Capsule Endoscopy with Focus on Data Efficiency and Transparency
(Doctoral theses at NTNU;2023:272, Doctoral thesis, 2023)Over the past decade the use of deep learning in different application areas has increased steadily. These areas include automated decisions in industries to autonomous vehicles, defence, and even medical. Each field gives ... -
Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey
(Peer reviewed; Journal article, 2021)With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution ... -
Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks
(Peer reviewed; Journal article, 2021)5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing ... -
Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes
(Peer reviewed; Journal article, 2022)Purpose Deep learning models excel in classifying medical image data but give little insight into the areas identified as pathology. Visualization of a deep learning model’s point of interest (POI) may reveal unexpected ... -
Deep learning in Dynamic Imager - A convolutional neural network module
(Master thesis, 2018)This thesis investigates the extent of which deep learning methods can be used for automatic detection of salmon in images. It also investigates the extent of which a module with deep learning functionality can be integrated ... -
Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead
(Peer reviewed; Journal article, 2021)Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective ... -
Deep Learning Methods for Classification of Photometric Images of Materials
(Master thesis, 2022)A key topic in the field of computer vision is image classification, which involves predicting one class for each input image. Additionally, one of its tasks is the categorization of materials from images, which is difficult ... -
Deep learning models for optically characterizing 3D printers
(Peer reviewed; Journal article, 2021)Multi-material 3D printers are able to create material arrangements possessing various optical properties. To reproduce these properties, an optical printer model that accurately predicts optical properties from the printer’s ...