Sökning: "RGBD"

Visar resultat 1 - 5 av 7 uppsatser innehållade ordet RGBD.

  1. 1. Pose Estimation and 3D Reconstruction for 3D Dispensing

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

    Författare :Subramanian Murali Ram; [2020]
    Nyckelord :;

    Sammanfattning : Currently in most of the cases the material deposition or dispensing is done only on planar surfaces, in applications such as extrusion 3D printing and surface mount technology (SMT) electronics assembly solutions. In future, the dispensing will be carried out on arbitrary three dimensional objects, where the dispenser needs to know the exact shape and location of them. LÄS MER

  2. 2. Manipulation Action Recognition and Reconstruction using a Deep Scene Graph Network

    Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologi; Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Dawid Ejdeholm; Jacob Harsten; [2020]
    Nyckelord :;

    Sammanfattning : Convolutional neural networks have been successfully used in action recognition but are usually restricted to operate on Euclidean data, such as images. In recent years there has been an increase in research devoted towards finding a generalized model operating on non-Euclidean data (e. LÄS MER

  3. 3. Improving deep monocular depth predictions using dense narrow field of view depth images

    Master-uppsats, KTH/Robotik, perception och lärande, RPL

    Författare :Christoffer Möckelind; [2018]
    Nyckelord :Deep learning; Monocular; Depth estimation; Narrow field of view; RGB; RGBD; Noicy depth; Dense depth; Narrow depth; Sparse depth;

    Sammanfattning : In this work we study a depth prediction problem where we provide a narrow field of view depth image and a wide field of view RGB image to a deep network tasked with predicting the depth for the entire RGB image. We show that by providing a narrow field of view depth image, we improve results for the area outside the provided depth compared to an earlier approach only utilizing a single RGB image for depth prediction. LÄS MER

  4. 4. Incorporating Scene Depth in Discriminative Correlation Filters for Visual Tracking

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :John Stynsberg; [2018]
    Nyckelord :Tracking; Visual; Deep; Learning; Machine; Learning; CNN; Convolutional; Neural; Network; Unsupervised; Learning; Clustering; Genetic Algorithms; Features; Visual featues; Channel; Coding; RGBD; Scene; Depth; Map; Kinect; Discriminative; Correlation; Filters; SRDCF; DCF; Spatial; Spatially; Regularized; Hyperparameter; Search; Occlusion; Detection; Handling; Kalman; Filters; Normalized; Convolution; Bayesian; Gaussian; Mixture; Scale; Estimation; Conjugate; Gradient; Linkoping; Sweden; Visuell; Följning; Särdrag; Djupa; Faltningsnätverk; Maskininlärning; Djup; Inlärning; Genetiska; Algoritmer; Klustring; Djup; RGBD; Linköping; Sverige;

    Sammanfattning : Visual tracking is a computer vision problem where the task is to follow a targetthrough a video sequence. Tracking has many important real-world applications in several fields such as autonomous vehicles and robot-vision. LÄS MER

  5. 5. Combining dense short range sensors and sparse long range sensors for mapping

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

    Författare :Ismael Lin; [2018]
    Nyckelord :3D Mapping; Kinect sensor; LiDAR; SLAM;

    Sammanfattning : Mapping is one of the main components of autonomous robots, and consist in the construction of a model of their environment based on the information gathered by different sensors over time. Those maps will have different attributes depending on the type of sensor used for the reconstruction. In this thesis we focus on RGBD cameras and LiDARs. LÄS MER