高级检索
    朱 峰, 罗立民, 宋余庆, 陈健美, 左 欣. 基于自适应空间邻域信息高斯混合模型的图像分割[J]. 计算机研究与发展, 2011, 48(11): 2000-2007.
    引用本文: 朱 峰, 罗立民, 宋余庆, 陈健美, 左 欣. 基于自适应空间邻域信息高斯混合模型的图像分割[J]. 计算机研究与发展, 2011, 48(11): 2000-2007.
    Zhu Feng, Luo Limin, Song Yuqing, Chen Jianmei, Zuo Xin. Adaptive Spatially Neighborhood Information Gaussian Mixture Model for Image Segmentation[J]. Journal of Computer Research and Development, 2011, 48(11): 2000-2007.
    Citation: Zhu Feng, Luo Limin, Song Yuqing, Chen Jianmei, Zuo Xin. Adaptive Spatially Neighborhood Information Gaussian Mixture Model for Image Segmentation[J]. Journal of Computer Research and Development, 2011, 48(11): 2000-2007.

    基于自适应空间邻域信息高斯混合模型的图像分割

    Adaptive Spatially Neighborhood Information Gaussian Mixture Model for Image Segmentation

    • 摘要: 针对高斯混合模型用于图像分割时仅利用了像素的灰度信息,而忽视空间位置信息,导致在噪声区域和边界处有误分割现象,提出一种自适应空间邻域信息高斯混合模型的图像分割法.该方法定义了一个能够有力抑制噪声点、很好保留边界特性的自适应空间邻域信息函数.在此基础上,设计了每个像素由某个类生成的邻域信息加权概率,并证明了该加权概率满足归一性和空间连续性2个准则;最后,利用EM优化算法给出模型参数E步和M步迭代求解公式.通过人工合成图像与真实图像的实验表明,该方法具有满意的分割效果.

       

      Abstract: One of the important characteristics of an image is that neighborhood pixels are highly correlated. In other words, these neighboring pixels possess similar feature values and the probability that they belong to the same cluster is great. Unfortunately,the application of Gaussian mixture model to image segmentation has not been taken into account spatial information except for intensity values, which could lead to misclassification on the boundaries and inhomogeneous regions with noise. In order to solve this problem, a new image segmentation method using adaptive spatially neighborhood information Gaussian mixture model without any control paremeters is proposed in this paper. Firstly, an adaptive spatial information function is defined to deal with the neighbour pixel of spatial correlatation,which is not only effective to deal with noise, but also to reserve well edge property. Secondly, it designs the neighbour information weighted class probabilities of every pixel according to Bayesian rules and proves that these class probabilities satisfy two norms of polarity and spatial continuity. Finally, an expectation maximization algorithm is used to obtain iterative formula of E-step and M-step as an optimization method. The experiments by synthetic images and real images demonstrate that the proposed method can obtain a better classification result and less effect on the noise.

       

    /

    返回文章
    返回