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The algorithm consists of online and offline stages.
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In the Offline stage we form the μ-independent quantities.
The order reduced model is built upon high-fidelity finite element approximations during the offline stage.
The coefficients needed for this approximation are computed in the offline stage by inverting an inexpensive linear system.
To overcome this issue, we propose an RB approach to compute in an "offline" stage LOD for suitable representative parameters.
In the offline stage, we get the depth map of the real scene using a low cost RGB-D camera.
The hit efficiency was estimated to be 92±2% using cosmic-ray muons, after noise reduction at the offline stage.
Local multiscale methods often construct multiscale basis functions in the offline stage without taking into account input parameters, such as source terms, boundary conditions, and so on.
While specific data collection and positioning are computed in the offline stage, specific data exchange is performed in the online stage.
To accomplish the above tasks simultaneously, we propose a novel occlusion handling method based on 3D reconstruction, which consists of offline stage and online stage.
In the offline stage, a new depth image dataset has been recorded and labeled, and the labeled images have been used to train a classifier.
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