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基于DCNN的图像语义分割综述
引用本文:魏云超,赵耀. 基于DCNN的图像语义分割综述[J]. 北京交通大学学报(自然科学版), 2016, 40(4): 82-91. DOI: 10.11860/j.issn.1673-0291.2016.04.013
作者姓名:魏云超  赵耀
作者单位:北京交通大学 计算机与信息技术学院,北京,100044;北京交通大学 计算机与信息技术学院,北京,100044
基金项目:国家科技重大专项资金资助(2016YFB0800404)
摘    要:图像的语义分割是计算机视觉中重要的基本问题之一,其目标是对图像的每个像素点进行分类,将图像分割为若干个视觉上有意义的或感兴趣的区域,以利于后续的图像分析和视觉理解.近年来,深度卷积神经网络(Deep Convolutional Neural Network,DCNN)的出现,极大地推动了语义分割的发展.本文从语义分割的基本定义出发,对语义分割中存在的困难和挑战进行了分析和描述.总结了目前用于评测语义分割算法的典型数据库,并以PASCAL VOC数据库为主线对近年来基于DCNN的语义分割算法进行了梳理和总结.最后对语义分割未来的研究重点进行了探讨和预测.

关 键 词:图像语义分割  深度学习  深度卷积神经网络

A review on image semantic segmentation based on DCNN
WEI Yunchao,ZHAO Yao. A review on image semantic segmentation based on DCNN[J]. JOURNAL OF BEIJING JIAOTONG UNIVERSITY, 2016, 40(4): 82-91. DOI: 10.11860/j.issn.1673-0291.2016.04.013
Authors:WEI Yunchao  ZHAO Yao
Abstract:Image semantic segmentation is one of the most important problems in computer vision, whose target is to assign a semantic label for each pixel of a given image and segment the image into several visually meaningful or interest regions,in order for the image analysis and vision un-derstanding .In recent years,tremendous progress has been made for semantic segmentation due to the development of deep convolutional neural network (DCNN).In this paper,we firstly dis-cuss the difficulties and challenges of semantic segmentation.Then,DCNN-based achievements in the study of semantic segmentation are reviewed.In addition,an analysis for semantic segmen-tation based on PASCAL VOC dataset is given,which is generally acknowledged as a popular public evaluation for semantic segmentation.Finally,some probable development directions of semantic segmentation are discussed.
Keywords:image semantic segmentation  deep learning  deep convolutional neural network
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