This paper proposes an algorithm based on curve evolution for unsupervised texture segmentation.
提出了一种基于曲线演化的非监督式纹理分割算法。
Three images results of segmentation are presented and demonstrate the efficiency of QPSO algorithms to automatic and unsupervised texture segmentation.
给出了三幅图像的分割效果，证明了QPSO算法在自动的和无监督的纹理分割上具有很好的效果。
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。
In order to overcome the weakness of statistical texture segmentation method, a new unsupervised texture segment algorithm, based on multi-resolution statistical model, was present.
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。
In order to overcome the weakness of statistical texture segmentation method, a new unsupervised texture segment algorithm, based on multi-resolution statistical model, was present.
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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