Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut
In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object dete...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7411 |
| _version_ | 1797476893869473792 |
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| author | Fuzhou Duan Yanyan Wu Hongliang Guan Chenbo Wu |
| author_facet | Fuzhou Duan Yanyan Wu Hongliang Guan Chenbo Wu |
| author_sort | Fuzhou Duan |
| collection | DOAJ |
| description | In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation. |
| first_indexed | 2024-03-09T21:11:13Z |
| format | Article |
| id | doaj.art-8649cf453e1f491384420d5a0cbfd5a4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:11:13Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8649cf453e1f491384420d5a0cbfd5a42023-11-23T21:48:43ZengMDPI AGSensors1424-82202022-09-012219741110.3390/s22197411Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCutFuzhou Duan0Yanyan Wu1Hongliang Guan2Chenbo Wu3Engineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, ChinaEngineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, ChinaEngineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, ChinaEngineering Research Center of Spatial Information Technology, MOE, Capital Normal University, 105 West Third Ring North Road, Haidian District, Beijing 100048, ChinaIn the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.https://www.mdpi.com/1424-8220/22/19/7411salient object detectionlight fieldforeground mattingGrabCut |
| spellingShingle | Fuzhou Duan Yanyan Wu Hongliang Guan Chenbo Wu Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut Sensors salient object detection light field foreground matting GrabCut |
| title | Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut |
| title_full | Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut |
| title_fullStr | Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut |
| title_full_unstemmed | Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut |
| title_short | Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut |
| title_sort | saliency detection of light field images by fusing focus degree and grabcut |
| topic | salient object detection light field foreground matting GrabCut |
| url | https://www.mdpi.com/1424-8220/22/19/7411 |
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