Sökning: "semantic segmentation"

Visar resultat 16 - 20 av 155 uppsatser innehållade orden semantic segmentation.

1. 16. Self-supervised pre-training of an attention-based model for 3D medical image segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Albert Sund Aillet; [2023]
   Nyckelord :Computer vision; Deep learning; 3D Medical image segmentation; Self-supervised learning; Datorseende; Djupinlärning; 3D Medicinsk bildsegmentering; Självövervakad träning;

   Sammanfattning : Accurate segmentation of anatomical structures is crucial for radiation therapy in cancer treatment. Deep learning methods have been demonstrated effective for segmentation of 3D medical images, establishing the current standard. However, they require large amounts of labelled data and suffer from reduced performance on domain shift. LÄS MER

3. 17. Evaluation of Ferroelectric Tunnel Junction memristor for in-memory computation in real world use cases

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Alec Guerin; Christos Papadopoulos; [2023]
   Nyckelord :FTJ; Ferroelectric Tunneling Junction; Analog in-memory computing; AIMC; Memristor; A.I.; AIHWKIT; Semantic segmentation; Natural Language Processing; NLP; Neuromorphic Computing; Matrix Vector Multiplication; Technology and Engineering;

   Sammanfattning : Machine learning algorithms are experiencing unprecedented attention, but their inherent computational complexity leads to high energy consumption. However, a paradigm shift in computing methods has the potential to address the issue. LÄS MER

4. 18. Land Use/Land Cover Classification From Satellite Remote Sensing Images Over Urban Areas in Sweden : An Investigative Multiclass, Multimodal and Spectral Transformation, Deep Learning Semantic Image Segmentation Study

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Oskar Aidantausta; Patrick Asman; [2023]
   Nyckelord :data fusion; deep learning; land use land cover classification; multiclass; multimodal; remote sensing; semantic segmentation; Sentinel satellite; spectral index; U-Net; Urban Atlas;

   Sammanfattning : Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. LÄS MER

5. 19. Skyline Delineation for Localization in Occluded Environments : Improved Skyline Delineation using Environmental Context from Deep Learning-based Semantic Segmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Kyle William Coble; [2023]
   Nyckelord :Skyline delineation; Skyline detection; Semantic segmentation; Terrain based navigation; Digital elevation models; Uncrewed surface vessel; Planetary exploration robots; Horisont avgränsning; Horisont upptäckt; Semantisk segmentering; Terrängbaserad navigering; Digitala höjdmodeller; Obemannat ytfartyg; Planetariska utforskningsrobotar;

   Sammanfattning : This thesis addresses the problem of improving the delineation of skylines, also referred to as skyline detection, in occluded and challenging environments where existing skyline delineation methods may struggle or fail. Delineated skylines can be used in monocular camera localization methods by comparing delineated skylines to digital elevation model data to estimate a position based on known terrain. LÄS MER

7. 20. Enhancement-basedSmall TargetDetection for InfraredImages

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yang Hanqi; [2023]
   Nyckelord :Infrared Images; Small targets; Dilated Convolution; Infraröda bilder; Små mål; Dilaterad konvolution;

   Sammanfattning : Infrared small target detection is widely used in fields such as military and security. UNet, which is a classical semantic segmentation method proposed in 2015, has shown excellent performance and robustness. However, U-Net suffers from the problem of losing small targets in deep layers after multiple down-sampling operations. LÄS MER