摘要
本文结合基于全卷积神经网络的语义分割与基于水平集的图像分割方法,使用DeepLab V2与Distance Regularized Level Set Evolution(DRLSE)模型对一般彩色图像进行分割.通过在DRLSE模型中加入一个新形状能量项,该方法提高了零水平集的演化速度.数值模拟结果验证了方法的有效性.
In this study,we combine the semantic segmentation technology based on full convolution neural network and the image segmentation technology based on level set method and uses Deeplab V2 and Distance Regularized Level Set Evolution(DRLSE)model to realize general color image segmentation.To improve the evolution speed of the zero level set segmentation,a new shape energy term is added to the DRLSE model.Numerical simulations verify the efficiency of our method.
作者
杨宇
崔陶
YANG Yu;CUI Tao(School of Mathematics,Sichuan University,Chengdu 610064,China;West China Second University Hospital,Sichuan University,Chengdu 610041,China;Key Laboratory of Birth Defects and Related Diseases of Women and Children(Sichuan University),Ministry of Education,Chengdu 610041,China)
出处
《四川大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第4期21-32,共12页
Journal of Sichuan University(Natural Science Edition)
基金
国家重点研发计划(2018YFC0830300)
科技部重点研发计划(“十三五”)(2020YFC2005603)
四川省科技厅重点研发项目(2020YFS0206)
国家自然科学基金(11971020)。
