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首页> 外文会议>International conference on graphic and image processing >A Robust Fuzzy Local Information C-Means Clustering Algorithm with Noise Detection
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A Robust Fuzzy Local Information C-Means Clustering Algorithm with Noise Detection

机译：具有噪声检测的鲁棒模糊局部信息C均值聚类算法

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Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.
机译：模糊c均值聚类（FCM），尤其是具有空间约束（FCM_S）的聚类算法，是一种适用于图像分割的有效算法。它的可靠性不仅有助于表示每个像素的归属性的模糊性，而且还有助于开发空间上下文信息。但是这些算法在用噪声处理图像时仍然存在一些问题，它们对必须根据噪声的先验知识进行调整的参数敏感。在本文中，我们提出了一种新的FCM算法，将灰色约束和空间约束相结合，称为空间和灰度级去噪模糊c均值（SGDFCM）算法。通过考虑每个像素噪声的可能性，该新算法克服了上述参数缺点，旨在提高鲁棒性并获得更多细节信息。此外，可以提前计算出噪声的可能性，这意味着该算法是有效且高效的。

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