Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images
Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/5/2515 |
| _version_ | 1797475692529582080 |
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| author | Shudi Yang Jiaxiong Wu Zhipeng Feng |
| author_facet | Shudi Yang Jiaxiong Wu Zhipeng Feng |
| author_sort | Shudi Yang |
| collection | DOAJ |
| description | Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining. |
| first_indexed | 2024-03-09T20:47:37Z |
| format | Article |
| id | doaj.art-5f1d57097f8a44d693a217e95e8a0c96 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:47:37Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5f1d57097f8a44d693a217e95e8a0c962023-11-23T22:42:04ZengMDPI AGApplied Sciences2076-34172022-02-01125251510.3390/app12052515Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater ImagesShudi Yang0Jiaxiong Wu1Zhipeng Feng2School of Mechanical Engineering, University of Science and Technology, Beijing 100083, ChinaSchool of Mechanical Engineering, University of Science and Technology, Beijing 100083, ChinaSchool of Mechanical Engineering, University of Science and Technology, Beijing 100083, ChinaUnderwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.https://www.mdpi.com/2076-3417/12/5/2515underwater imagetarget contour extractionactive contour modelsemantic informationsaliency target |
| spellingShingle | Shudi Yang Jiaxiong Wu Zhipeng Feng Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images Applied Sciences underwater image target contour extraction active contour model semantic information saliency target |
| title | Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images |
| title_full | Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images |
| title_fullStr | Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images |
| title_full_unstemmed | Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images |
| title_short | Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images |
| title_sort | dual fusion active contour model with semantic information for saliency target extraction of underwater images |
| topic | underwater image target contour extraction active contour model semantic information saliency target |
| url | https://www.mdpi.com/2076-3417/12/5/2515 |
| work_keys_str_mv | AT shudiyang dualfusionactivecontourmodelwithsemanticinformationforsaliencytargetextractionofunderwaterimages AT jiaxiongwu dualfusionactivecontourmodelwithsemanticinformationforsaliencytargetextractionofunderwaterimages AT zhipengfeng dualfusionactivecontourmodelwithsemanticinformationforsaliencytargetextractionofunderwaterimages |