This paper proposes a new neural network model UMAN to perform unsupervised image segmentation.
本文提出了一种新的神经网络模型UMAN，以实现非监督的图象分割。
This paper presents a novel unsupervised image segmentation algorithm based on hidden Markov random field(HMRF) model.
研究了基于隐马尔可夫随机场（HMRF）模型的无监督图像分割问题。
Furthermore, in order to solve the model selection problems of unsupervised image segmentation, the sum of squared error criterion with penalty term is proposed.
而且，针对无监督图像分割的模型选择问题提出了带惩罚项的误差平方和阶次判定准则。
An unsupervised segmentation of SAR imagery based on Multiscale image block is proposed.
提出了一种基于多尺度图像块的SAR图像无监督分割方法。
This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the 'image, and finish the automatic segmentation.
这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数，从而完成对图像的自动分割。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
This paper presents a novel approach to unsupervised texture segmentation according to a very general nonparametric statistical model of image neighborhoods.
针对基于统计的纹理分割算法存在的不足，提出了一种新的多分辨模型下的无监督统计纹理分割算法。
This paper presents a novel approach to unsupervised texture segmentation according to a very general nonparametric statistical model of image neighborhoods.
针对基于统计的纹理分割算法存在的不足，提出了一种新的多分辨模型下的无监督统计纹理分割算法。
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