Hanning Wang, Jiang Wang, Chuangzhan Zeng, Chen Wang
{"title":"非交互式GrabCut图像分割方法","authors":"Hanning Wang, Jiang Wang, Chuangzhan Zeng, Chen Wang","doi":"10.1117/12.3000781","DOIUrl":null,"url":null,"abstract":"The GrabCut image segmentation algorithm based on the principle of graph theory has been extensively used in the field of computer vision. However, the shortcoming is that it requires human-computer interaction to complete the ROI region selection to solve the segmentation task of the foreground image. Therefore, it cannot meet the requirements of fully intelligent image processing. In order to eliminate human-computer interaction and realize smart region selection, this paper proposes a ROI smart region generating and fine-tuning method to improve the GrabCut method, so as to realize intelligent image segmentation. The experimental results show that our method is compatible with both single-target and multi-target foreground image segmentation solutions.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"145 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-interactive GrabCut image segmentation method\",\"authors\":\"Hanning Wang, Jiang Wang, Chuangzhan Zeng, Chen Wang\",\"doi\":\"10.1117/12.3000781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The GrabCut image segmentation algorithm based on the principle of graph theory has been extensively used in the field of computer vision. However, the shortcoming is that it requires human-computer interaction to complete the ROI region selection to solve the segmentation task of the foreground image. Therefore, it cannot meet the requirements of fully intelligent image processing. In order to eliminate human-computer interaction and realize smart region selection, this paper proposes a ROI smart region generating and fine-tuning method to improve the GrabCut method, so as to realize intelligent image segmentation. The experimental results show that our method is compatible with both single-target and multi-target foreground image segmentation solutions.\",\"PeriodicalId\":210802,\"journal\":{\"name\":\"International Conference on Image Processing and Intelligent Control\",\"volume\":\"145 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Image Processing and Intelligent Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.3000781\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Image Processing and Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.3000781","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The GrabCut image segmentation algorithm based on the principle of graph theory has been extensively used in the field of computer vision. However, the shortcoming is that it requires human-computer interaction to complete the ROI region selection to solve the segmentation task of the foreground image. Therefore, it cannot meet the requirements of fully intelligent image processing. In order to eliminate human-computer interaction and realize smart region selection, this paper proposes a ROI smart region generating and fine-tuning method to improve the GrabCut method, so as to realize intelligent image segmentation. The experimental results show that our method is compatible with both single-target and multi-target foreground image segmentation solutions.