CN113435389B - 基于图像特征深度学习的小球藻和金藻分类识别方法 - Google Patents
基于图像特征深度学习的小球藻和金藻分类识别方法 Download PDFInfo
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Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107578060A (zh) * | 2017-08-14 | 2018-01-12 | 电子科技大学 | 一种基于可判别区域的深度神经网络用于菜品图像分类的方法 |
CN107977671A (zh) * | 2017-10-27 | 2018-05-01 | 浙江工业大学 | 一种基于多任务卷积神经网络的舌象分类方法 |
CN108304812A (zh) * | 2018-02-07 | 2018-07-20 | 郑州大学西亚斯国际学院 | 一种基于卷积神经网络和多视频图像的作物病害识别方法 |
CN109949284A (zh) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | 基于深度学习卷积神经网络的水藻细胞分析方法及系统 |
CN109977780A (zh) * | 2019-02-26 | 2019-07-05 | 广东工业大学 | 一种基于深度学习算法的硅藻的检测与识别方法 |
CN110245562A (zh) * | 2019-05-13 | 2019-09-17 | 中国水产科学研究院东海水产研究所 | 基于深度学习的海洋产毒微藻种类自动识别方法 |
CN110321967A (zh) * | 2019-07-11 | 2019-10-11 | 南京邮电大学 | 基于卷积神经网络的图像分类改进算法 |
CN110532941A (zh) * | 2019-08-27 | 2019-12-03 | 安徽生物工程学校 | 一种常见藻类的特征图像提取方法 |
KR20200023221A (ko) * | 2018-08-23 | 2020-03-04 | 서울대학교산학협력단 | 딥러닝 기반의 실시간 대상 추적 방법 및 시스템 |
AU2020101229A4 (en) * | 2020-07-02 | 2020-08-06 | South China University Of Technology | A Text Line Recognition Method in Chinese Scenes Based on Residual Convolutional and Recurrent Neural Networks |
CN111783590A (zh) * | 2020-06-24 | 2020-10-16 | 西北工业大学 | 一种基于度量学习的多类别小目标检测方法 |
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Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107578060A (zh) * | 2017-08-14 | 2018-01-12 | 电子科技大学 | 一种基于可判别区域的深度神经网络用于菜品图像分类的方法 |
CN107977671A (zh) * | 2017-10-27 | 2018-05-01 | 浙江工业大学 | 一种基于多任务卷积神经网络的舌象分类方法 |
CN108304812A (zh) * | 2018-02-07 | 2018-07-20 | 郑州大学西亚斯国际学院 | 一种基于卷积神经网络和多视频图像的作物病害识别方法 |
KR20200023221A (ko) * | 2018-08-23 | 2020-03-04 | 서울대학교산학협력단 | 딥러닝 기반의 실시간 대상 추적 방법 및 시스템 |
CN109977780A (zh) * | 2019-02-26 | 2019-07-05 | 广东工业大学 | 一种基于深度学习算法的硅藻的检测与识别方法 |
CN109949284A (zh) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | 基于深度学习卷积神经网络的水藻细胞分析方法及系统 |
CN110245562A (zh) * | 2019-05-13 | 2019-09-17 | 中国水产科学研究院东海水产研究所 | 基于深度学习的海洋产毒微藻种类自动识别方法 |
CN110321967A (zh) * | 2019-07-11 | 2019-10-11 | 南京邮电大学 | 基于卷积神经网络的图像分类改进算法 |
CN110532941A (zh) * | 2019-08-27 | 2019-12-03 | 安徽生物工程学校 | 一种常见藻类的特征图像提取方法 |
CN111783590A (zh) * | 2020-06-24 | 2020-10-16 | 西北工业大学 | 一种基于度量学习的多类别小目标检测方法 |
AU2020101229A4 (en) * | 2020-07-02 | 2020-08-06 | South China University Of Technology | A Text Line Recognition Method in Chinese Scenes Based on Residual Convolutional and Recurrent Neural Networks |
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