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Wyszukujesz frazę "point feature histogram" wg kryterium: Temat


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Tytuł:
Understanding 3D shapes being in motion
Autorzy:
Bedkowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/385100.pdf
Data publikacji:
2013
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
RGB-D camera
point cloud
normal vector estimation
point feature histogram
parallel programming
Opis:
This paper concerns a classification problem of 3D shapes being in motion. The goal is to develop the system with real-time capabilities to distinguish basic shapes (corners, planes, cones, spheres etc.) that are moving in front of RGB-D sensor. It is introduced an improvement of SoA algorithms (normal vector computation using PCA Principal Component Analysis and SVD Singular Value Decomposition, PFH – Point Feature Histogram) based on GPGPU (General Purpose Graphic Processor Unit) computation. This approach guarantee on-line computation of normal vectors, unfortunately computation time of the PFH for each normal vector is still a challenge to obtain on-line capabilities, therefore in this paper it is shown how to find a region of movement and to perform the classification process assuming the decreased amount of data. Proposed approach can be a starting point for further developments of the systems able to recognize the objects in the dynamic environments.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2013, 7, 1; 42-46
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recognition of Hand Posture Based on a Point Cloud Descriptor and a Feature of Extended Fingers
Autorzy:
Warchoł, D.
Wysocki, M.
Powiązania:
https://bibliotekanauki.pl/articles/384737.pdf
Data publikacji:
2016
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
hand posture recognition
depth cameras
Kinect
point cloud
Viewpoint Feature Histogram
Opis:
Our work involves hand posture recognition based on 3D data acquired by the KinectTM sensor in the form of point clouds. We combine a descriptor built on the basis of the Viewpoint Feature Histogram (VFH) with additional feature describing the number of extended fingers. First, we extract a region corresponding to the hand and then a histogram of the edge distances from the palm center is built. Based on quantized version of the histogram we calculate the number of extended fingers. This information is used as a first feature describing the hand which, together with VFH-based features, form the feature vector. Before calculating VFH we rotate the hand making our method invariant to hand rotations around the axis perpendicular to the camera lens. Finally, we apply nearest neighbor technique for the posture classification. We present results of crossvalidation tests performed on a representative dataset consisting of 10 different postures, each shown 10 times by 10 subjects. The comparison of recognition rate and mean computation time with other works performed on this dataset confirms the usefulness of our approach.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 1; 48-57
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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