Techniques and Challenges of Image Segmentation: A Review
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 achieve...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/5/1199 |
| _version_ | 1797615499356405760 |
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| author | Ying Yu Chunping Wang Qiang Fu Renke Kou Fuyu Huang Boxiong Yang Tingting Yang Mingliang Gao |
| author_facet | Ying Yu Chunping Wang Qiang Fu Renke Kou Fuyu Huang Boxiong Yang Tingting Yang Mingliang Gao |
| author_sort | Ying Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:26:13Z |
| format | Article |
| id | doaj.art-1e77b06497f64df6ba87ee5a3ee17684 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:26:13Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1e77b06497f64df6ba87ee5a3ee176842023-11-17T07:33:02ZengMDPI AGElectronics2079-92922023-03-01125119910.3390/electronics12051199Techniques and Challenges of Image Segmentation: A ReviewYing Yu0Chunping Wang1Qiang Fu2Renke Kou3Fuyu Huang4Boxiong Yang5Tingting Yang6Mingliang Gao7Department of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaDepartment of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaDepartment of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaDepartment of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaDepartment of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, ChinaSchool of Information and Intelligent Engineering, University of Sanya, Sanya 572022, ChinaSchool of Information and Intelligent Engineering, University of Sanya, Sanya 572022, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, ChinaImage 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.https://www.mdpi.com/2079-9292/12/5/1199image segmentationco-segmentationsemantic segmentationdeep learningimage processing |
| spellingShingle | Ying Yu Chunping Wang Qiang Fu Renke Kou Fuyu Huang Boxiong Yang Tingting Yang Mingliang Gao Techniques and Challenges of Image Segmentation: A Review Electronics image segmentation co-segmentation semantic segmentation deep learning image processing |
| title | Techniques and Challenges of Image Segmentation: A Review |
| title_full | Techniques and Challenges of Image Segmentation: A Review |
| title_fullStr | Techniques and Challenges of Image Segmentation: A Review |
| title_full_unstemmed | Techniques and Challenges of Image Segmentation: A Review |
| title_short | Techniques and Challenges of Image Segmentation: A Review |
| title_sort | techniques and challenges of image segmentation a review |
| topic | image segmentation co-segmentation semantic segmentation deep learning image processing |
| url | https://www.mdpi.com/2079-9292/12/5/1199 |
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