基于神经网络与模糊算法图像边缘检测方法*

针对传统图像边缘检测方法中出现毛边、噪边、边缘定位不精确等缺点，提出一种神经网络与模糊算法相结合的检测方法。根据图像特征，将图像分为高频和低频部分分别处理，高频部分适宜用双层网络结构，可以很好地减弱噪声；对于图像低频部分，将模糊理论引入到边缘检测中，能够检测出弱边。最后对检测出的两个图像边缘进行融合，实验结果证明得出的检测效果较好，比传统边缘检测算子所获结果有很大改善。

Image edge detection based on neural network and fuzzy algorithm

Considering the disadvantages in the traditional image edge detection methods,such as rough edge,noise edge and inaccurate edge location,this paper proposed a novel edge detection approach which was the combination of neural network with fuzzy algorithm to solve above problems.First,according to the character of image,the image was divided into high frequency and low frequency,and processed respectively.It applied the method of LVQ(learning vector quantization) double-layer neural network to the high frequency part of image.The noise could be suppressed effectively with this method.Then using the fuzzy algorithm for the low frequency part of image,thus weak edges could be detected with high quality,and the target could be separated from the background clearly.Finally,combined the two images from former methods.The experimental result demonstrates that the effect of the edge detection is greatly improved comparing with the traditional edge detection methods.