Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology
Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved signif...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9187673/ |
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| _version_ | 1841557009227317248 |
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| author | Xinghuo Ye Qianyi Wang |
| author_facet | Xinghuo Ye Qianyi Wang |
| author_sort | Xinghuo Ye |
| collection | DOAJ |
| description | Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively. |
| format | Article |
| id | doaj-art-4d7b2066ceb545a19bcfb777cb7e41ef |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4d7b2066ceb545a19bcfb777cb7e41ef2025-01-07T00:01:02ZengIEEEIEEE Access2169-35362021-01-019191871919410.1109/ACCESS.2020.30220119187673Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing TechnologyXinghuo Ye0https://orcid.org/0000-0002-4062-1704Qianyi Wang1https://orcid.org/0000-0001-9697-6865School of Information Engineering, Huzhou University, Huzhou, ChinaAI Lab Artificial Intelligence Laboratory, Beijing Chengshi Wanglin Information Technology Company Ltd., Beijing, ChinaImage segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.https://ieeexplore.ieee.org/document/9187673/ |
| spellingShingle | Xinghuo Ye Qianyi Wang Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology IEEE Access |
| title | Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology |
| title_full | Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology |
| title_fullStr | Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology |
| title_full_unstemmed | Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology |
| title_short | Retracted: Active Contour Image Segmentation Method for Training Talents of Computer Graphics and Image Processing Technology |
| title_sort | retracted active contour image segmentation method for training talents of computer graphics and image processing technology |
| url | https://ieeexplore.ieee.org/document/9187673/ |
| work_keys_str_mv | AT xinghuoye retractedactivecontourimagesegmentationmethodfortrainingtalentsofcomputergraphicsandimageprocessingtechnology AT qianyiwang retractedactivecontourimagesegmentationmethodfortrainingtalentsofcomputergraphicsandimageprocessingtechnology |