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In the first method, we may not append multiple objects with the same name.
On the other hand, the second method allows multiple objects with the same name to be prepended.
By producing the edge map of objects by curvelet thresholding instead of simple gradient methods, the proposed method shows great promises to recognize edges of multiple objects with weak edges and strong noises.
To address this issue, our handoff algorithm employs an adaptive resource management mechanism to dynamically allocate cameras' resources to multiple objects with different priorities so that the required minimum frame rate is maintained.
Namely, those involve independent tracking of multiple objects with a Kalman filter assigned to each object (which will be referred to as KF-based tracking), or joint tracking using PF based on importance sampling (shortly, SIS-based tracking).
While there exist reliable algorithms for the tracking of a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple objects with complex dynamics.
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Brain plasticity is especially stimulated by complex bimanual tasks, because, as for juggling, they require simultaneous control of multiple movements, high level of bimanual coordination, balance and sustained swapping attention to multiple objects interacting with both hands.
Algorithm 1 summarizes the conditions for multiple objects association with the coverage uncertainty.
We propose a new multi-target tracking approach, which is able to reliably track multiple objects even with poor segmentation results due to noisy environments.
The second example (see Figures 4 and 5) shows that our model can work well for images with noise or non-uniform background and images with multiple objects of inhomogeneous intensities, starting with a constant function.
pml-layers can be zero or more pml objects, with multiple objects allowing you to specify different PML layers on different boundaries.
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