Sökning: "Deep machine learning for gesture"

Hittade 4 uppsatser innehållade orden Deep machine learning for gesture.

  1. 1. 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

  3. 2. Exploiting Leap Motion and Microsoft Kinect Sensors for Static and Dynamic Sign Gesture Recognition

    Magister-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Sumit Rakesh; [2021]
    Nyckelord :;

    Sammanfattning : One of the primary ways of communication between humans is verbal communication. Among hearing-impaired persons, the traditional way of communication is through sign language. Sign gestures are the atomic actions used in sign language for non-verbal communication. LÄS MER

  4. 3. sEMG Classication with Convolutional Neural Networks: A Multi-Label Approach for Prosthetic Hand Control

    Master-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknik

    Författare :Alexander Olsson; [2018]
    Nyckelord :Electromyography; Machine Learning; Deep Learning; Gesture Recognition; Neural Networks; Technology and Engineering;

    Sammanfattning : In myoelectric prosthesis design, there is often a trade-off between control robustness and range of executable movements. As a low movement error rate is necessary in any real application, this often results in a quite severe limitation on the dexterity of the user. LÄS MER

  5. 4. 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