Sökning: "segmentering bilder"

Visar resultat 1 - 5 av 82 uppsatser innehållade orden segmentering bilder.

1. 1. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Thomas Proudhon; [2023]
   Nyckelord :Cardiac Magnetic Resonance Imaging; Deep Learning; Domain Adaptation; Unsupervised Segmentation; Image-to-image Translation;

   Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. LÄS MER

3. 2. Automatisk detektering av kretskorts position inom in-circuit testning

   Uppsats för yrkesexamina på grundnivå, Högskolan i Gävle/Datavetenskap

   Författare :Dennis Hellstadius; [2023]
   Nyckelord :;

   Sammanfattning : Detta arbete har försökt att skapa ett program som kan användas för att hitta position och vinklar av kretskort för ett automatiserat Bed-of-Nails test. Programmet har haft som krav att kunna räkna ut vilken position i en bricka kretskort ligger i, vilken vinkel kretskortet ligger i gentemot brickan samt hur många kretskort som finns i brickan. LÄS MER

4. 3. Development of a Complete Minuscule Microscope: Embedding Data Pipeline and Machine Learning Segmentation

   Master-uppsats, KTH/Tillämpad fysik

   Författare :Kenan Zec; [2023]
   Nyckelord :Incubation-microscope; Machine Learning Segmentation; ESP32-Cam; Deep Learning; Inkubationsmikroskop; Maskininlärnings-segmentering; ESP32-Cam; Deep Learning;

   Sammanfattning : Cell culture is a fundamental procedure in many laboratories and precedes much research performed under the microscope. Despite the significance of this procedural stage, the monitoring of cells throughout growth is impossible due to the absence of equipment and methodological approaches. LÄS MER

5. 4. Developing a Neural Network Model for Semantic Segmentation

   M1-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Ronny Westphal; [2023]
   Nyckelord :Semantic segmentation; neural network; Unity Barracuda; PyTorch; augmented reality; Semantisk segmentering; neurala nätverk; Unity Barracuda; PyTorch; augmenterad verklighet;

   Sammanfattning : This study details the development of a neural network model designed for real-time semantic segmentation, specifically to distinguish sky pixels from other elements within an image. The model is incorporated into a feature for an Augmented Reality application in Unity, leveraging Unity Barracuda—a versatile neural network inference library. LÄS MER

7. 5. Self-learning for 3D segmentation of medical images from single and few-slice annotation

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

   Författare :Côme Lassarat; [2023]
   Nyckelord :Self-supervised Learning; Segmentation; Medical images; Självövervakad inlärning; segmentering; medicinska bilder;

   Sammanfattning : Training deep-learning networks to segment a particular region of interest (ROI) in 3D medical acquisitions (also called volumes) usually requires annotating a lot of data upstream because of the predominant fully supervised nature of the existing stateof-the-art models. To alleviate this annotation burden for medical experts and the associated cost, leveraging self-learning models, whose strength lies in their ability to be trained with unlabeled data, is a natural and straightforward approach. LÄS MER