Sökning: "gestigenkänning"

Visar resultat 1 - 5 av 8 uppsatser innehållade ordet gestigenkänning.

1. 1. A Comparative Study on the Importance of Image Resolution in Gesture Recognition

   Kandidat-uppsats, KTH/Datavetenskap

   Författare :Klara Alpsten; Tora Wallerö; [2022]
   Nyckelord :;

   Sammanfattning : Sign language translation applications could provide a whole new avenue of communication. However, translating sign language comes with challenges such as deriving and handling information from images which can be a difficult task for computers. LÄS MER

3. 2. Real-time hand segmentation using deep learning

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

   Författare :Federico Favia; [2021]
   Nyckelord :Hand Segmentation; Semantic Segmentation; Deep Learning; Convolutional Neural Networks; Real-time; Augmented Reality; Embedded Devices; Dataset; Transfer Learning; Handsegmentering; Semantisk Segmentering; Djupinlärning; Konvolutionsneurala Nätverk; Realtid; Förstärkt Verklighet; Inbäddade Enheter; Datauppsättning; Transferlärning;

   Sammanfattning : Hand segmentation is a fundamental part of many computer vision systems aimed at gesture recognition or hand tracking. In particular, augmented reality solutions need a very accurate gesture analysis system in order to satisfy the end consumers in an appropriate manner. Therefore the hand segmentation step is critical. LÄS MER

4. 3. MobilePose: Real-Time 3D Hand Pose Estimation from a Single RGB Image

   Master-uppsats, Lunds universitet/Matematik LTH

   Författare :David Larsson; Sofie Hellmark; [2020]
   Nyckelord :3D hand pose estimation; real-time; Convolutional neural networks; Image analysis; Machine learning; AR; Mathematics and Statistics;

   Sammanfattning : Estimating 3D hand poses from RGB images is a challenging task. In this work we construct efficient neural networks to regress sparse 3D skeletons consisting of 21 keypoints in the hand. Additionally heatmaps are regressed to locate the keypoints in 2D. LÄS MER

5. 4. Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Benny Lam; Jakob Nilsson; [2019]
   Nyckelord :AR; Hand gestures; Bare-hand interaction; HGR; Augmented reality; usability; user experience; Manomotion; ARKit; Visual Odometry; SLAM; VO; VIO; 3D gestural interaction; gesture recognition; gesture tracking; augmented environments; Förstärkt verklighet; 3D gestinteraktion; gestigenkänning; visuell odometri; Manomotion; ARKit;

   Sammanfattning : The dissemination of new innovative technology requires feasibility and simplicity. The problem with marker-based augmented reality is similar to glove-based hand gesture recognition: they both require an additional component to function. LÄS MER

7. 5. 3D Hand Pose Tracking from Depth Images using Deep Reinforcement Learning

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

   Författare :Sneha Saha; [2018]
   Nyckelord :;

   Sammanfattning : Low-cost consumer depth cameras have enabled reasonable 3D hand pose trackingfrom single depth images. Such 3D hand pose tracking can be an integralpart of many computer vision applications such as gesture recognition and humanactivity tracking. LÄS MER