A Method of Edge Detection Combined by Discrete Information Entropy and Self -Organizing Map (SOM)

Jianhua Wu, Kun Wang, Liqun Gao, Zhengang Shi, Zhaoyu Pian
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Abstract

An edge detection method by combining image entropy and self -organizing map (SOM) is proposed in this paper. First, discrete information entropy is used to curve up the smooth region and the region of gray level abruptly changed. This can decrease the latter process. Then we transform the gray level image to ideal binary pattern of pixels. We define six classes' edge and six edge prototype vectors. These edge prototype vectors are fed into input layer of the self-organizing map (SOM). Classifying the type of edge through this network, the edge image is obtained. At last, the speckle edges are discarded from the edge image. Experimental results show that it gained better edge image compared with other edge detection methods.
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一种基于离散信息熵和自组织映射的边缘检测方法
提出了一种结合图像熵和自组织映射(SOM)的边缘检测方法。首先,利用离散信息熵对平滑区域和灰度突变区域进行曲线化处理;这可以减少后一个过程。然后将灰度图像转换为理想的像素二值模式。我们定义了6个类的边和6个边原型向量。这些边缘原型向量被输入到自组织映射(SOM)的输入层。通过该网络对边缘类型进行分类,得到边缘图像。最后,从边缘图像中去除散斑边缘。实验结果表明,与其他边缘检测方法相比,该方法获得了更好的边缘图像。
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