CN106570851B - 一种基于加权分配d-s证据理论的显著图融合方法 - Google Patents
一种基于加权分配d-s证据理论的显著图融合方法 Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Publication number | Priority date | Publication date | Assignee | Title |
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CN107977948B (zh) * | 2017-07-25 | 2019-12-24 | 北京联合大学 | 一种面向社群图像的显著图融合方法 |
CN108647567B (zh) * | 2018-03-29 | 2021-10-29 | 中国人民解放军61540部队 | 基于条件证据理论的场景可识别性分析方法 |
CN108694710A (zh) * | 2018-04-18 | 2018-10-23 | 大连理工大学 | 一种基于(n)模糊积分的显著图融合方法 |
CN109035267B (zh) * | 2018-06-22 | 2021-07-27 | 华东师范大学 | 一种基于深度学习的图像目标抠取方法 |
CN112101161B (zh) * | 2020-09-04 | 2022-06-07 | 西安交通大学 | 基于相关系数距离与迭代改进的证据理论故障状态识别方法 |
CN112733915B (zh) * | 2020-12-31 | 2023-11-07 | 大连大学 | 基于改进d-s证据理论的态势估算方法 |
CN114034338B (zh) * | 2021-10-29 | 2023-08-11 | 国网安徽省电力有限公司电力科学研究院 | 一种基于改进d-s证据理论的开关柜多源参量监测方法 |
CN117077987B (zh) * | 2023-10-16 | 2024-01-02 | 湖南省通晓信息科技有限公司 | 一种基于元胞自动机的环卫管理方法及存储介质 |
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CN101996157A (zh) * | 2010-10-23 | 2011-03-30 | 山东科技大学 | 证据高冲突环境下多源信息融合方法 |
CN102968786A (zh) * | 2012-10-23 | 2013-03-13 | 西北工业大学 | 一种非监督的遥感图像潜在目标区域检测方法 |
CN103810526A (zh) * | 2014-01-28 | 2014-05-21 | 北京仿真中心 | 一种基于d-s证据理论的知识融合方法 |
CN106056165A (zh) * | 2016-06-28 | 2016-10-26 | 大连理工大学 | 一种基于超像素关联性增强Adaboost分类学习的显著性检测方法 |
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Publication number | Priority date | Publication date | Assignee | Title |
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CN101996157A (zh) * | 2010-10-23 | 2011-03-30 | 山东科技大学 | 证据高冲突环境下多源信息融合方法 |
CN102968786A (zh) * | 2012-10-23 | 2013-03-13 | 西北工业大学 | 一种非监督的遥感图像潜在目标区域检测方法 |
CN103810526A (zh) * | 2014-01-28 | 2014-05-21 | 北京仿真中心 | 一种基于d-s证据理论的知识融合方法 |
CN106056165A (zh) * | 2016-06-28 | 2016-10-26 | 大连理工大学 | 一种基于超像素关联性增强Adaboost分类学习的显著性检测方法 |
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