{"title":"基于视觉注意模型和分水岭分割的兴趣区域提取","authors":"J. Zhang, L. Zhuo, Lansun Shen","doi":"10.1109/ICNNSP.2008.4590375","DOIUrl":null,"url":null,"abstract":"The presented research addressed a novel visual attention model and watershed segmentation based approach of regions of interest (ROIs) extraction, which automatically extracts ROIs and copes with the watershed over-segmentation. This approach uses visual attention model to locate salient points, in which the winner point, the most salient point, is selected as the seed point of watershed segmentation. ROIs are extracted by combining salient regions with watershed segmented regions. The focus of attention (FOA) is shifted to measure the importance or interest of the extracted regions. The experimental results show that the proposed method is effective to reduce over-segmentation in auto-extracting ROIs and performs well for different objects.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"173 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Regions of Interest extraction based on visual attention model and watershed segmentation\",\"authors\":\"J. Zhang, L. Zhuo, Lansun Shen\",\"doi\":\"10.1109/ICNNSP.2008.4590375\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The presented research addressed a novel visual attention model and watershed segmentation based approach of regions of interest (ROIs) extraction, which automatically extracts ROIs and copes with the watershed over-segmentation. This approach uses visual attention model to locate salient points, in which the winner point, the most salient point, is selected as the seed point of watershed segmentation. ROIs are extracted by combining salient regions with watershed segmented regions. The focus of attention (FOA) is shifted to measure the importance or interest of the extracted regions. The experimental results show that the proposed method is effective to reduce over-segmentation in auto-extracting ROIs and performs well for different objects.\",\"PeriodicalId\":250993,\"journal\":{\"name\":\"2008 International Conference on Neural Networks and Signal Processing\",\"volume\":\"173 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Neural Networks and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNNSP.2008.4590375\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Neural Networks and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNNSP.2008.4590375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Regions of Interest extraction based on visual attention model and watershed segmentation
The presented research addressed a novel visual attention model and watershed segmentation based approach of regions of interest (ROIs) extraction, which automatically extracts ROIs and copes with the watershed over-segmentation. This approach uses visual attention model to locate salient points, in which the winner point, the most salient point, is selected as the seed point of watershed segmentation. ROIs are extracted by combining salient regions with watershed segmented regions. The focus of attention (FOA) is shifted to measure the importance or interest of the extracted regions. The experimental results show that the proposed method is effective to reduce over-segmentation in auto-extracting ROIs and performs well for different objects.