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The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed.
This can be achieved using the mean shift algorithm.
Thus, the image is segmented using the mean shift method according to [22].
The set of regions is obtained by a color-based segmentation using the mean shift method [13].
Using the mean shift algorithm as introduced in [6], AbdAllah and Shimshoni [1] considered handling incomplete data by means of so-called MD(_{E}) distance; but did not supply any incomplete data handling scheme.
The tracked target is divided into several fragments by integrating the log-likelihood ratio image and morphological operation, and each fragment is tracked through a kernel using the mean shift procedure.
The best result obtained by using the mean shift algorithm is because the mean shift is a good low pass filter.
This proves the advantage of the new algorithm with regard to the previous one because, besides being simpler, this doesn't use the parameter, M. Nevertheless, the algorithms that used the mean shift were superior, in this application, to Otsu's method.
Throughout this work we use the mean-shift tracker.
Up to 295 university students took part in the experiments and all trajectories were extracted using the mean-shift algorithm.
It used the mean-shift method to approximate the local maximum value of the density function.
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