Techniques and Challenges of Image Segmentation: A Review
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Published:2023-03-02
Issue:5
Volume:12
Page:1199
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Yu Ying12, Wang Chunping1, Fu Qiang1, Kou Renke1, Huang Fuyu1, Yang Boxiong2ORCID, Yang Tingting2, Gao Mingliang3ORCID
Affiliation:
1. Department of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, China 2. School of Information and Intelligent Engineering, University of Sanya, Sanya 572022, China 3. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Abstract
Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques.
Funder
National Natural Science Foundation of China Hainan Provincial Natural Science Foundation of China Specific Research Fund of The Innovation Platform for Academicians of Hainan Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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