摘要
以肺表面纹理为研究对象,提出一种基于卷积神经网络的纹理合成算法。算法以训练好的VGG-19模型为基础,用Gram矩阵来表示纹理的局部结构特征,引入一种结构约束关系来捕捉纹理的全局结构特征,并在网络的高层加入马尔科夫随机场以提高算法效率。实验结果表明该算法可以较好地合成具有局部结构特征以及非局部结构特征的肺表面纹理,并且对比现有相似的算法效率有了明显提升。
Taking the surface texture of lung as the research object,this paper proposes a texture synthesis algorithm based on convolutional neural network.Based on the trained VGG-19 model,the algorithm uses the Gram matrix to represent the local structural features of the texture,introduces a structural constraint relationship to capture the global structural features of the texture,and joins the Markov random field at the upper level of the network to improve algorithm efficiency.The experimental results show that the algorithm can synthesize the lung surface texture with local structural features and non-local structural features,and the efficiency of the existing similar algorithms is significantly improved.
作者
陈国栋
潘冠慈
田影
CHEN Guo-dong;PAN Guan-ci;TIAN Ying(College of Physics and Information Engineering,Fuzhou University,Fujian Fuzhou 350116,China)
出处
《佳木斯大学学报（自然科学版）》
CAS
2019年第1期32-35,93,共5页
Journal of Jiamusi University：Natural Science Edition
基金
福建省自然科学基金项目(2016J01293)
福建省自然科学基金(2017J01107)
