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After smoothing, the first image is segmented into different regions by a mean shift algorithm.
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The new position of the object is found in the peak of the map by using a mean shift.
Matched data were analysed by paired t test, and showed a mean shift in confidence from 63% (SD 14.6) before training to 77% (SD 12.3) after training (t=15.6; n=186, p<0.001).
In the background anti-matching phrase, a certain number of background regions are extracted based on the feature of color orientation codes via an entropy filter, and the covariance matrix is adapted to match these regions to get the global motion of the background; further, the object matching is carried out by a mean-shift tracking algorithm.
Therefore, if we consider entropy as a measure of the disorder within a system, it could be used as a good stopping criterion for an iterative process, by using mean shift filtering.
In this work, entropy is used as the stopping criterion when segmenting an image by recursively applying mean shift filtering.
Let (h x,t, h y,t ) be the hand coordination calculated by the mean shift.
The homogeneous color regions in which the propagation is performed are obtained by the mean shift color segmentation algorithm.
The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable.
The energy functional in the higher dimensional space obtains the region parameters through the weighted kernel function by utilising mean shift technique.
During the tracking the kernel scale is first adapted to the current object size by running the mean shift iterations in an extended search space.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com