[1]张 婧,张 策*,张 茹,等.图像分割述评:基本概貌、典型算法及比较分析[J].计算机技术与发展,2024,34(01):1-8.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 001]
　ZHANG Jing,ZHANG Ce*,ZHANG Ru,et al.Review of Image Segmentation:Basic Overview,Typical Algorithms and Comparative Analysis[J].,2024,34(01):1-8.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 001]
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图像分割述评:基本概貌、典型算法及比较分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年01期
页码:
1-8
栏目:
综述
出版日期:
2024-01-10

文章信息/Info

Title:
Review of Image Segmentation:Basic Overview,Typical Algorithms and Comparative Analysis
文章编号:
1673-629X(2024)01-0001-08
作者:
张 婧1 ; 张 策1* ; 张 茹2 ; 王宇彬1 ; 张 展3 ; 苏子旸1 ; 吕为工1
1. 哈尔滨工业大学(威海) 计算机科学与技术学院,山东 威海 264209;
2. 哈尔滨商业大学 管理学院,黑龙江 哈尔滨 150076;
3. 哈尔滨工业大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
Author(s):
ZHANG Jing1 ; ZHANG Ce1* ; ZHANG Ru2 ; WANG Yu-bin1 ; ZHANG Zhan3 ; SU Zi-yang1 ; LYU Wei-gong1
1. School of Computer Science and Technology,Harbin Institute of Technology ( Weihai) ,Weihai 264209,China;
2. School of Management,Harbin University of Commerce,Harbin 150076,China;
3. School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China
关键词:
图像分割; 分割算法; 语义分割; 深度学习; 像素分类
Keywords:
image segmentation; segmentation algorithm; semantic segmentation; deep learning; pixel classification
分类号:
TP311
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 01. 001
摘要:
图像分割作为计算机视觉领域的一个重要分支,在可穿戴计算、自动驾驶、医学图像分析等方面都发挥着重要作用,并有着广泛应用。 为了更好地了解图像分割领域的发展以及研究现状,该文对图像分割进行了深入梳理和系统述评。首先,对图像分割的含义以及其工作流程、指标等进行阐释;然后,对图像分割方法按照时间的跨度进行分类———基于阈值和区域、基于图论和聚类,以及基于深度学习的图像分割,对每类方法的代表性算法进行分析介绍,较为全面地总结了每类方法的基本思想和优缺点;最后,对该领域目前存在的问题和未来的发展方向进行展望,提出实时图像语义分割、弱监督或非监督语义分割和三维场景的语义分割是目前研究中的主要挑战。
Abstract:
As an important field of computer vision, image segmentation plays an important role in medical image analysis, automaticdriving,wearable computing and so on,and has wide application. In order to better understand the development and research status ofimage segmentation,we make a thorough review and systematic review of image segmentation. Firstly,we explain the meaning of imagesegmentation and its workflow and indicators. Then,the image segmentation algorithms are classified according to the time span - basedon threshold and region,based on graph theory and clustering,as well as image segmentation based on deep learning. The representativealgorithms of each type of methods are analyzed and introduced,and their basic ideas,advantages and disadvantages are comprehensivelysummarized. Finally,we look forward to the current problems and future development direction in this field. Real-time image semanticsegmentation,weakly supervised or unsupervised semantic segmentation and three-dimensional scene semantic segmentation are the mainchallenges in current research.

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更新日期/Last Update: 2024-01-10