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Further, we prove that, for all kernels, the mean shift procedure is a quadratic bound maximization.
Zhou et al. [17] employed the mean shift procedure for volume segmentation.
We did not aim to exceed the quality of the original mean shift procedure.
In the second step of this algorithm, the mean shift procedure is initialized from only these representative leaf elements resulting in modes.
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.
Comaniciu 8 has proven that the mean shift procedure converges.
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In other words, if one considers the recursive procedure as the sum of many procedures of the mean shift, and each individual procedure converges, the recursive procedure then converges as well.
The steps of the developed previously binarization algorithm are the following: Run the mean shift-filtering procedure according to former steps, obtaining at Z the filtered image.
In other words, if one considers the recursive procedure to be like the individual sum of many procedures of the mean shift and each individual procedure converges, then the recursive procedure also converges.
We build on the current understanding of mean shift as an optimization procedure.
In this work, after the segmented image was obtained based on the recursively application of mean shift filtering, a binarization procedure was carried out according to algorithm No. 2. Some details on the original images are given next.
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