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Visar resultat 1 - 5 av 135 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Automatic Semantic Segmentation of Indoor Datasets

    Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Sai Swaroop Rachakonda; [2024]
    Nyckelord :Semantic Segmentation; Annotation; SLAM; Indoor datasets; YOLO V8; DETIC; Segment Anything Model.;

    Sammanfattning : Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. LÄS MER

  3. 2. Replacing Objects in Point Cloud stream with Real-time Meshes using Semantic Segmentation

    Master-uppsats, Blekinge Tekniska Högskola

    Författare :Abhinav Chitta; [2024]
    Nyckelord :;

    Sammanfattning : Background: The evolving landscape of 3D data processing, particularly pointcloud manipulation, is pivotal in numerous applications ranging from architecturaldesign to spatial analysis. Traditional methods, primarily mesh generation from point clouds, face challenges in adapting to complex real-world scenarios. LÄS MER

  4. 3. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

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

    Författare :Xinchen Wang; [2024]
    Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

    Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

  5. 4. Image Segmentation and Object Identification in Cancer Tissue Slides from Fluorescence Microscopy

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3

    Författare :Sebastian Eriksson; Fredrik Forsberg; [2023]
    Nyckelord :image analysis; cancer research; fluorescent microscopy; image segmentation; bildanalys; cancerforskning; fluorescensmikroskopi; bildsegmentering;

    Sammanfattning : In cancer research, there is a need to make accurate spatial measurements in multi-layered fluorescence microscopy images. Researchers would like to measure distances in and between biological objects such as nerves and tumours, to investigate questions which includes if nerve distribution in and around tumours can have a prognostic value in cancer diagnostics. LÄS MER

  7. 5. Instance segmentation using 2.5D data

    Master-uppsats, Linköpings universitet/Institutionen för systemteknik

    Författare :Jonathan Öhrling; [2023]
    Nyckelord :instance segmentation; multi-modality; segmentation; multi-modality fusion; CNN; RGBD; ToF; Mask R-CNN; RTMDet; MMDetection; COCO; NYUDepth;

    Sammanfattning : Multi-modality fusion is an area of research that has shown promising results in the domain of 2D and 3D object detection. However, multi-modality fusion methods have largely not been utilized in the domain of instance segmentation. LÄS MER