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False-Peaks-Avoiding Mean Shift Method for Unsupervised Peak-Valley Sliding Image Segmentation

机译:避免针对无监督峰谷滑动图像分割的平均移位方法

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The mean shift (MS) algorithm is sensitive to local peaks. In this paper, we show both empirically and analytically that when using sample data, the reconstructed PDF may have false peaks. We show how the occurrence of the false peaks is related to the bandwidth h of the kernel density estimator, using examples of gray-level image segmentation. It is well known that in MS-based approaches, the choice of h is important: we provide a quantitative relationship between false peaks and h. For the gray-level image segmentation problem, we provide a complete unsupervised peak-valley sliding algorithm for gray-level image segmentation.
机译:平均移位(MS)算法对本地峰值敏感。在本文中,我们在经验和分析地显示,当使用样本数据时,重建的PDF可能具有假峰。我们展示了使用灰度级图像分割的示例,假峰的发生与内核密度估计器的带宽H有关。众所周知,在基于MS的方法中,H的选择很重要:我们提供假峰和H之间的定量关系。对于灰度级图像分割问题,我们为灰度级图像分割提供了一种完整的无监督峰值滑动算法。

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