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首页> 外文期刊>International Journal of Fuzzy Systems >A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model
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A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model

机译：基于高斯模型的彩色平面与混合高斯模型相互作用的复杂彩色图像分割模糊聚类方法

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摘要

In complex color images, colors inside a homogeneous region might be contradistinctive and the distribution could not be described by a simple Gaussian distribution as used in traditional image segmentation algorithms. Based on the characteristics that the red, green, and blue color planes are not independent and pixels in the same neighborhood system might stand for the same object, we introduce a Gaussian model containing the interactions between different color planes to strengthen the connections both on a color plane and between color planes in a neighborhood system. Consequently, a Gaussian mixture model with the prior distribution, defined by Markov random field and acting as the weight, is employed to describe the distribution of color measures inside a homogeneous region. With the Gaussian mixture model containing the interactions between color planes, we proposed a fuzzy clustering approach for complex color image segmentation. Experiments on synthetic and real-color images, in which homogeneous regions are complex, show that the proposed algorithm compares favorably with the compared algorithms developed for the same purpose.
机译：在复杂的彩色图像中，同质区域内的颜色可能是不同的，并且该分布无法用传统图像分割算法中使用的简单高斯分布来描述。基于红色，绿色和蓝色颜色平面不独立且同一邻域系统中的像素可能代表同一对象的特征，我们引入了一个高斯模型，其中包含不同颜色平面之间的相互作用，以增强两个颜色平面之间的连接。颜色平面以及邻域系统中的颜色平面之间。因此，采用具有先验分布的高斯混合模型（由马尔可夫随机场定义并作为权重）来描述均匀区域内颜色度量的分布。利用包含色彩平面之间相互作用的高斯混合模型，我们提出了一种用于复杂彩色图像分割的模糊聚类方法。在合成图像和实色图像（均质区域复杂）上进行的实验表明，该算法与为相同目的而开发的算法相比具有优势。

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