基于深度学习和水平集的彩色图像分割方法

A color image segmentation method based on deep learning and level set

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.