摘要
为了解图像分割领域的研究现状,对图像分割方法进行了系统性梳理,首先按照基于阈值、边缘、区域、聚类、图论及特定理论等6类方法介绍传统图像分割方法;然后介绍基于深度学习的分割方法,并探讨了几种常用的分割网络模型,包括全卷积网络(full convolutional network,FCN)、金字塔场景解析网络(pyramid scene parsing network,PSPNet)、DeepLab、Mask R-CNN;最后在图像分割的常用数据集上对同类方法进行了性能比较和分析。
In order to understand the current research status in the field of image segmentation,the image segmentation methods are systematically sorted out.Firstly,traditional image segmentation methods are introduced according to 6 types of methods based on thresholds,edges,regions,clusters,graph theory,and specific theories.Then the segmentation methods based on deep learning are introduced,and several commonly used segmentation network models are discussed,including full convolutional network(FCN),pyramid scene parsing network(PSPNet),DeepLab,and Mask R-CNN.Finally,the performance comparison and analysis of similar methods are performed on the commonly used datasets for image segmentation.
作者
黄鹏
郑淇
梁超
HUANG Peng;ZHENG Qi;LIANG Chao(School of Computer Science,Wuhan University,Wuhan 430072,Hubei,China;Shenzhen Institute of Wuhan University,Shenzhen 518000,Guangzhou,China)
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2020年第6期519-531,共13页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金(61876135,U1903214,61862015)
湖北省自然科学基金(2019CFB472,2018AAA062,2018CFA024)
深圳市科技计划基础研究项目(JCYJ20170818143246278)。
关键词
图像处理
图像分割
深度学习
image processing
image segmentation
deep learning
