CN111353988A - Knn动态自适应的双图卷积图像分割方法和系统 - Google Patents
Knn动态自适应的双图卷积图像分割方法和系统 Download PDFInfo
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- CN111353988A CN111353988A CN202010138819.XA CN202010138819A CN111353988A CN 111353988 A CN111353988 A CN 111353988A CN 202010138819 A CN202010138819 A CN 202010138819A CN 111353988 A CN111353988 A CN 111353988A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
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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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111931859A (zh) * | 2020-08-28 | 2020-11-13 | 中国科学院深圳先进技术研究院 | 一种多标签图像识别方法和装置 |
CN111985542A (zh) * | 2020-08-05 | 2020-11-24 | 华中科技大学 | 代表性图结构模型、视觉理解模型的建立方法及应用 |
CN112053362A (zh) * | 2020-07-14 | 2020-12-08 | 北京百度网讯科技有限公司 | 图像分割方法、装置、电子设备及存储介质 |
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CN109543589A (zh) * | 2018-11-16 | 2019-03-29 | 西安电子科技大学 | 基于初相-多普勒不变距离和knn的海陆场景分割方法 |
CN109753589A (zh) * | 2018-11-28 | 2019-05-14 | 中国科学院信息工程研究所 | 一种基于图卷积网络的图可视化方法 |
CN110717526A (zh) * | 2019-09-23 | 2020-01-21 | 华南理工大学 | 一种基于图卷积网络的无监督迁移学习方法 |
CN110853072A (zh) * | 2019-11-08 | 2020-02-28 | 安徽大学 | 基于自引导推理的弱监督图像语义分割方法 |
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Patent Citations (4)
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CN109543589A (zh) * | 2018-11-16 | 2019-03-29 | 西安电子科技大学 | 基于初相-多普勒不变距离和knn的海陆场景分割方法 |
CN109753589A (zh) * | 2018-11-28 | 2019-05-14 | 中国科学院信息工程研究所 | 一种基于图卷积网络的图可视化方法 |
CN110717526A (zh) * | 2019-09-23 | 2020-01-21 | 华南理工大学 | 一种基于图卷积网络的无监督迁移学习方法 |
CN110853072A (zh) * | 2019-11-08 | 2020-02-28 | 安徽大学 | 基于自引导推理的弱监督图像语义分割方法 |
Non-Patent Citations (3)
Title |
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MADHUMITA KEDLAYA ET.AL.: ""Novel KNN-Motivation-PSO and its Application to Image Segmentation"", 《CUBE 2012》 * |
王方等: ""基于优化神经网络的地质灾害监测预警仿真"", 《计算机仿真》 * |
王旭娇等: ""基于图卷积网络的深度学习点云分类模型"", 《激光与光电子学进进展》 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112053362A (zh) * | 2020-07-14 | 2020-12-08 | 北京百度网讯科技有限公司 | 图像分割方法、装置、电子设备及存储介质 |
CN112053362B (zh) * | 2020-07-14 | 2024-02-06 | 北京百度网讯科技有限公司 | 图像分割方法、装置、电子设备及存储介质 |
CN111985542A (zh) * | 2020-08-05 | 2020-11-24 | 华中科技大学 | 代表性图结构模型、视觉理解模型的建立方法及应用 |
CN111931859A (zh) * | 2020-08-28 | 2020-11-13 | 中国科学院深圳先进技术研究院 | 一种多标签图像识别方法和装置 |
CN111931859B (zh) * | 2020-08-28 | 2023-10-24 | 中国科学院深圳先进技术研究院 | 一种多标签图像识别方法和装置 |
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Denomination of invention: KNN Dynamic Adaptive Dual Image Convolutional Image Segmentation Method and System Effective date of registration: 20230907 Granted publication date: 20210423 Pledgee: Chengdu financial holding Financing Guarantee Co.,Ltd. Pledgor: CHENGDU DACHENG JUNTU TECHNOLOGY CO.,LTD. Registration number: Y2023510000213 |