结合离散熵和自组织神经网络(SOM)的边缘检测方法

提出了一种结合图像离散熵和自组织神经网络的边缘检测方法.首先,用离散熵选定阈值来分割图像的平滑区域和灰度变化剧烈的区域,用来减少计算量;其次将灰度图像转化为理想二值像素模式;定义了6个边缘类型和6个原型向量.将这些边缘向量作为神经网络的输入,通过SOM对其进行边缘分类从而获得边缘图像.最后将斑点边缘从边缘图像中去除即得到理想的边缘图像.实验结果表明,与其他的边缘检测方法相比获得了较为理想的边缘.

Edge Detection Method Combing Discrete Information Entropy with Self-Organizing Map

An edge detection method is proposed combining image discrete information entropy with self-organizing map(SOM).A threshold is chosen from some different information entropies to segment the smooth region from the region where the gray level abruptly changes so as to reduce computation.Then,the gray level images are transformed into the ideal binary pattern of pixels.Six types of edge and six prototype vectors are defined,among them the latter are taken as inputs into SOM to classify the edge types and then obtain edge images from which the speckled edges are removed to acquire ideal edge images.Experimental results showed that the edge images gained by the method proposed are better than those by other edge detection methods.