Sökning: "semantic segmentation"

Visar resultat 1 - 5 av 79 uppsatser innehållade orden semantic segmentation.

  1. 1. Using Deep Learning to SegmentCardiovascular 4D Flow MRI : 3D U-Net for cardiovascular 4D flow MRI segmentation and Bayesian 3D U-Net for uncertainty estimation

    Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

    Författare :Omkar Bhutra; [2021]
    Nyckelord :Convolutional Neural Networks; 4D flow MRI; 3D U-Net; Cardiovascularimage segmentation; Uncertainty estimation; Bayesian 3D U-Net;

    Sammanfattning : Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. LÄS MER

  2. 2. Convolutional neural networks for semantic segmentation of FIB-SEM volumetric image data

    Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

    Författare :Fredrik Skärberg; [2020-11-26]
    Nyckelord :Deep learning; convolutional neural networks; image analysis; semantic segmentation; focused ion beam scanning electron microscopy; porous materials; controlled drug release;

    Sammanfattning : Focused ion beam scanning electron microscopy (FIB-SEM) is a well-established microscopytechnique for 3D imaging of porous materials. We investigate three poroussamples of ethyl cellulose microporous films made from ethyl cellulose and hydroxypropylcellulose (EC/HPC) polymer blends. LÄS MER

  3. 3. Training Multi-Task Deep Neural Networks with Disjoint Datasets

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

    Författare :Nik Vaessen; [2020]
    Nyckelord :;

    Sammanfattning : This work examines training neural networks which are capable of learning multiple tasks. We propose an architecture trained on KITTI and Cityscapes, which respectively include only the annotations for 2D object detection and semantic segmentation. LÄS MER

  4. 4. Semantic Scene Segmentation using RGB-D & LRF fusion

    Master-uppsats, Högskolan i Halmstad/CAISR Centrum för tillämpade intelligenta system (IS-lab)

    Författare :Harald Lilja; [2020]
    Nyckelord :RGB-D; LiDAR; CNN; deep multimodal fusion; robotics; autonomous vehicles;

    Sammanfattning : In the field of robotics and autonomous vehicles, the use of RGB-D data and LiDAR sensors is a popular practice for applications such as SLAM[14], object classification[19] and scene understanding[5]. This thesis explores the problem of semantic segmentation using deep multimodal fusion of LRF and depth data. LÄS MER

  5. 5. Domain Transfer for End-to-end Reinforcement Learning

    Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi; Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Anton Olsson; Felix Rosberg; [2020]
    Nyckelord :Reinforcement Learning; Domain Transfer; Deep Deterministic Policy Gradient; Reinforcement Learning in Real-time;

    Sammanfattning : In this master thesis project a LiDAR-based, depth image-based and semantic segmentation image-based reinforcement learning agent is investigated and compared forlearning in simulation and performing in real-time. The project utilize the Deep Deterministic Policy Gradient architecture for learning continuous actions and was designed to control a RC car. LÄS MER