Sökning: "traditional computer vision"

Visar resultat 1 - 5 av 64 uppsatser innehållade orden traditional computer vision.

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. 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

4. 3. Movement Estimation with SLAM through Multimodal Sensor Fusion

   Master-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakulteten

   Författare :Jimmy Cedervall Lamin; [2024]
   Nyckelord :slam; discrete-slam; continuous-slam; synchronous; asynchronous; computer vision; BRISK; opencv; ceres; visual; inertial; sensor fusion; multimodal; Simultaneous Localization and Mapping; time offset; pose estimation; quaternions; movement estimation;

   Sammanfattning : In the field of robotics and self-navigation, Simultaneous Localization and Mapping (SLAM) is a technique crucial for estimating poses while concurrently creating a map of the environment. Robotics applications often rely on various sensors for pose estimation, including cameras, inertial measurement units (IMUs), and more. LÄS MER

5. 4. Detecting Distracted Drivers using a Federated Computer Vision Model : With the Help of Federated Learning

   Kandidat-uppsats, Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

   Författare :Joel Viggesjöö; [2023]
   Nyckelord :Machine Learning; Federated Learning; Computer Vision;

   Sammanfattning : En av de vanligaste distraktionerna under bilkörning är utförandet av aktiviteter som avlägsnar förarens fokus från vägen, exempelvis användandet av en telefon för att skicka meddelanden. Det finns många olika sätt att hantera dessa problem, varav en teknik är att använda maskininlärning för att identifiera och notifiera distraherade bilförare. LÄS MER

7. 5. Deep Learning Model Deployment for Spaceborne Reconfigurable Hardware : A flexible acceleration approach

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

   Författare :Javier Ferre Martin; [2023]
   Nyckelord :Space Situational Awareness; Deep Learning; Convolutional Neural Networks; FieldProgrammable Gate Arrays; System-On-Chip; Computer Vision; Dynamic Partial Reconfiguration; High-Level Synthesis; Rymdsituationstänksamhet; Djupinlärning; Konvolutionsnätverk; Omkonfigurerbara Field-Programmable Gate Arrays FPGAs ; System-On-Chip SoC ; Datorseende; Dynamisk partiell omkonfigurering; Högnivåsyntes.;

   Sammanfattning : Space debris and space situational awareness (SSA) have become growing concerns for national security and the sustainability of space operations, where timely detection and tracking of space objects is critical in preventing collision events. Traditional computer-vision algorithms have been used extensively to solve detection and tracking problems in flight, but recently deep learning approaches have seen widespread adoption in non-space related applications for their high accuracy. LÄS MER