Exact(12)
In this paper, various robustness issues surrounding different types of second order shape estimates recovered from motion cue are addressed.
Figure 3 Real data observations: non-Gaussian, global symmetric shape, polarimetric width variations, zero-mean, and individual and overall shape estimates noted.
A mode matching method based on the characteristics of accurate mode shape estimates is proposed to ensure the correctness of the estimates.
The cod-end shape estimates were then entered into the simulation tool PRESEMO, to estimate their influence on the selectivity processes in the cod-end.
A mode matching method based on the characteristics of accurate mode shape estimates is then proposed to ensure the correctness of the estimates.
Recently, a sensitivity-based method was proposed for the normalization of operational mode shape estimates on the basis of in-operation modal models only.
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From top left clockwise, visual hull, 3D points generated by depth estimation, after outlier removal, shape estimated by implicit region growing, refined shape estimation by explicit surface evolution.
From top left clockwise, visual hull, 3D points generated by depth estimation, after outlier removal, shape estimated by implicit region growing, and refined shape estimation by explicit surface evolution.
Figure 2 A 2D illustration of the whole reconstruction pipeline: (a) visual hull; (b) points generated by depth estimation; (c) after outlier removal; (d) shape estimated by implicit region growing; (e) refined shape estimation by explicit surface evolution.
The iterative algorithm stems from the rather intuitive observation that a better estimate of the required initial shape can be obtained by adding a correction to the previous shape estimate equal to the computed mismatch in the deformed shapes.
The mean shift iterations are then performed as described in [17, 23] and the new position of the object as well as a scaled object shape will be determined, where the latter can be considered as a first shape estimate.
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