基于改进神经网络的图像边缘分割技术Image edge segmentation technology based on improved neural network
卫洪春
摘要(Abstract)：
采用梯度下降法进行图像边缘分割时受到噪声的干扰,训练过程中存在局部最佳解,从而导致图像边缘分割效果和泛化性能差。为此,提出基于改进神经网络的图像边缘分割方法,采集样本图像的中值特征量、基于梯度的特征量、Krisch算子方向特征量,融合三个特征向量塑造具备较强抗噪性能的样本图像特征向量,通过基于特征向量和BP神经网络的边缘检测算法,将样本图像特征向量输入四层BP神经网络,采用改进BP算法训练四层BP神经网络,采用训练后的改进神经网络完成图像边缘分割。实验结果表明,所提图像边缘分割方法细节保有性能强,分割精度和泛化能力强。
关键词(KeyWords)： 改进神经网络;图像边缘;图像分割;梯度特征;中值特征;改进BP算法
基金项目(Foundation): 四川省教育厅项目(15ZB0326);; 四川文理学院项目(2015TP003Y)~~
作者(Author): 卫洪春
DOI: 10.16652/j.issn.1004-373x.2018.16.028
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