{"title":"基于大脑双通道加工的动态场景视觉注意模型","authors":"Jiawei Chen, Changle Zhou, Kunhui Lin, Yanyun Qu","doi":"10.1109/ICNC.2008.392","DOIUrl":null,"url":null,"abstract":"In this paper, a visual attention model for dynamic scenes based on two-pathway processing in brain is proposed. A saliency map is created to measure the static features, and a motion conspicuity map is created to measure the dynamic feature. Both maps are integrated in an IFNN which is employed to simulate the interactive aspects of two-pathway processing in brain. Selection of attention is accomplished by the interspike interval of the neural network. The experiments results show the effective and efficient performance of the model.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":"1 1","pages":"128-132"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Visual Attention Model for Dynamic Scenes Based on Two-Pathway Processing in Brain\",\"authors\":\"Jiawei Chen, Changle Zhou, Kunhui Lin, Yanyun Qu\",\"doi\":\"10.1109/ICNC.2008.392\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a visual attention model for dynamic scenes based on two-pathway processing in brain is proposed. A saliency map is created to measure the static features, and a motion conspicuity map is created to measure the dynamic feature. Both maps are integrated in an IFNN which is employed to simulate the interactive aspects of two-pathway processing in brain. Selection of attention is accomplished by the interspike interval of the neural network. The experiments results show the effective and efficient performance of the model.\",\"PeriodicalId\":6404,\"journal\":{\"name\":\"2008 Fourth International Conference on Natural Computation\",\"volume\":\"1 1\",\"pages\":\"128-132\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Fourth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2008.392\",\"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 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Visual Attention Model for Dynamic Scenes Based on Two-Pathway Processing in Brain
In this paper, a visual attention model for dynamic scenes based on two-pathway processing in brain is proposed. A saliency map is created to measure the static features, and a motion conspicuity map is created to measure the dynamic feature. Both maps are integrated in an IFNN which is employed to simulate the interactive aspects of two-pathway processing in brain. Selection of attention is accomplished by the interspike interval of the neural network. The experiments results show the effective and efficient performance of the model.
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