基于形状描述符和孪生神经网络的纹理分割算法  

Texture segmentation based on shape descriptor and siamese neural network

作　　者：武海燕[1] 李卫平[1] WU Hai yan;LI Wei-Ping(Image and Network Investigation Department,Railway Police College,Zhengzhou 450053,China)

机构地区：[1]铁道警察学院图像与网络侦查系,河南郑州450053

出　　处：《微电子学与计算机》2020年第4期65-69,共5页Microelectronics & Computer

基　　金：河南省科技厅科技攻关计划项目(182102210118);河南省高等学校重点科研项目(18A520045);中央高校基本科研业务经费项目(2018TJJBKY017)。

摘　　要：为提升对复杂几何变换或复杂滋扰图像的分割效果,提出了一种新型的形状描述符及纹理分割算法.在感兴趣区域内聚合图像统计信息,通过神经网络训练得到形状描述符,利用训练得到的形状描述符进行纹理分割.分别对合成图像和真实图像进行分割实验,本文算法在轮廓指标和区域指标上均优于其他算法.实验结果表明,本文算法是有效可行的,并且能够对复杂几何变换或复杂滋扰图像取得较好的分割效果.To improve the segmentation effect of complex geometric transformation or disturbing images,a novel shape descriptor and texture segmentation algorithm is proposed.Image statistics are aggregated in the region of interest,shape descriptors are trained by neural network,and texture segmentation is carried out by using the trained shape descriptors.The segmentation experiments of synthetic image and real image show that the proposed algorithm is superior to other algorithms in contour index and region index.Experimental results show that the proposed algorithm is effective and feasible,and can achieve a better segmentation effect for complex geometric transformation or disturbing images.

关 键 词：纹理分割 形状描述符 孪生神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]