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Adaptive edge detection via image statistic features and hybrid model of fuzzy cellular automata and cellular learning automata

机译:自适应边缘检测通过图像统计特征和模糊蜂窝自动机和蜂窝学习自动机的混合模型

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In this paper a new approach for adaptive edge detection via image statistic features and hybrid model of fuzzy cellular automata and cellular learning automata is presented. Edge detection in image is one of the basic and most significant operations in image processing that edge detection have a lot of application in image processing. Presented method in first stage used of statistic feature of its image for primary edge detection, that cause adaptively for this method at all internal image. At the second stage fuzzy cellular automata and cellular learning automata are used for edges amplify and castrate these aren't edge. The result obtained from implementation shows That the performance of this method is much better Compared to other edge detection methods.
机译:本文提出了一种通过图像统计特征和模糊蜂窝自动机和蜂窝学习自动机的图像统计特征和混合模型的新方法。图像中的边缘检测是图像处理中的基本和最重要的操作之一,即边缘检测在图像处理中具有大量应用。呈现方法在第一阶段使用其用于主边缘检测的图像的统计特征,使得在所有内部图像处适用于该方法。在第二阶段模糊的蜂窝自动机和蜂窝学习自动机用于边缘和阉割这些不是边缘。从实施获得的结果表明,与其他边缘检测方法相比,该方法的性能要好得多。

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