Synthetic utilizing differential and filter operator to construct WNN for image edge detection

Maozhi Wang, Sheng Gou, Ke Guo
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Abstract

This paper constructs a Wavelet Neural Network (WNN) for image edge detection based on the fact that image edge detection is essentially a classification problem and WNN has powerful classification and identification capacity. The innovations of this paper include utilizing information of differential and filter operators in constructing the network and integrating the advantages of Canny and LOG operators in the selection of network training samples. Experimental results indicate that the method proposed in this paper can extract the image edge information effectively and the network presents good generalization ability. Also, a new edge detection based on wavelet transform of modulus maxima threshold is also proposed in this paper during the research on WNN. Finally, the selection of threshold, wavelet function and other parameters is discussed of WNN.
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综合利用微分算子和滤波算子构造小波神经网络进行图像边缘检测
基于图像边缘检测本质上是一个分类问题,而小波神经网络具有强大的分类识别能力,本文构建了一种用于图像边缘检测的小波神经网络。本文的创新点在于利用差分算子和滤波算子的信息构建网络,在网络训练样本的选择上综合了Canny算子和LOG算子的优势。实验结果表明,该方法能有效提取图像边缘信息,网络具有良好的泛化能力。在对小波神经网络的研究中,本文还提出了一种基于模极大值小波变换的边缘检测方法。最后讨论了小波神经网络的阈值、小波函数等参数的选择。
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