CN106651853B - 基于先验知识和深度权重的3d显著性模型的建立方法 - Google Patents
基于先验知识和深度权重的3d显著性模型的建立方法 Download PDFInfo
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- CN106651853B CN106651853B CN201611236297.7A CN201611236297A CN106651853B CN 106651853 B CN106651853 B CN 106651853B CN 201611236297 A CN201611236297 A CN 201611236297A CN 106651853 B CN106651853 B CN 106651853B
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- G—PHYSICS
- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/64—Three-dimensional objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
- G06V10/464—Salient features, e.g. scale invariant feature transforms [SIFT] using a plurality of salient features, e.g. bag-of-words [BoW] representations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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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
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
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CN201611236297.7A CN106651853B (zh) | 2016-12-28 | 2016-12-28 | 基于先验知识和深度权重的3d显著性模型的建立方法 |
US15/406,504 US10008004B1 (en) | 2016-12-28 | 2017-01-13 | Establishment method of 3D saliency model based on prior knowledge and depth weight |
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CN201611236297.7A CN106651853B (zh) | 2016-12-28 | 2016-12-28 | 基于先验知识和深度权重的3d显著性模型的建立方法 |
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CN106651853A CN106651853A (zh) | 2017-05-10 |
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Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
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US10587858B2 (en) * | 2016-03-14 | 2020-03-10 | Symbol Technologies, Llc | Device and method of dimensioning using digital images and depth data |
EP3252713A1 (en) * | 2016-06-01 | 2017-12-06 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Apparatus and method for performing 3d estimation based on locally determined 3d information hypotheses |
CN107145892B (zh) * | 2017-05-24 | 2019-01-22 | 北京大学深圳研究生院 | 一种基于自适应融合机制的图像显著性物体检测方法 |
CN110298782B (zh) * | 2019-05-07 | 2023-04-18 | 天津大学 | 一种rgb显著性到rgbd显著性的转换方法 |
CN110569857B (zh) * | 2019-07-28 | 2022-05-06 | 景德镇陶瓷大学 | 一种基于质心距离计算的图像轮廓角点检测方法 |
CN110570402B (zh) * | 2019-08-19 | 2021-11-19 | 浙江科技学院 | 基于边界感知神经网络的双目显著物体检测方法 |
CN110807776A (zh) * | 2019-09-09 | 2020-02-18 | 安徽省农业科学院农业经济与信息研究所 | 一种基于全局区域对比度的农作物半翅目害虫图像自动分割算法 |
CN111611999B (zh) * | 2020-05-22 | 2023-04-07 | 福建师范大学 | 一种融合小型深度生成模型的显著性检测方法及终端 |
KR20220028496A (ko) | 2020-08-28 | 2022-03-08 | 삼성전자주식회사 | 홀로그래픽 디스플레이 장치 및 방법 |
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CN105898278A (zh) * | 2016-05-26 | 2016-08-24 | 杭州电子科技大学 | 一种基于双目多维感知特性的立体视频显著性检测方法 |
CN106056596A (zh) * | 2015-11-30 | 2016-10-26 | 浙江德尚韵兴图像科技有限公司 | 基于局部先验信息和凸优化的全自动三维肝脏分割方法 |
CN106203430A (zh) * | 2016-07-07 | 2016-12-07 | 北京航空航天大学 | 一种基于前景聚集度和背景先验的显著性物体检测方法 |
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US20160189419A1 (en) * | 2013-08-09 | 2016-06-30 | Sweep3D Corporation | Systems and methods for generating data indicative of a three-dimensional representation of a scene |
KR101537174B1 (ko) * | 2013-12-17 | 2015-07-15 | 가톨릭대학교 산학협력단 | 스테레오스코픽 영상에서의 주요 객체 검출 방법 |
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CN106056596A (zh) * | 2015-11-30 | 2016-10-26 | 浙江德尚韵兴图像科技有限公司 | 基于局部先验信息和凸优化的全自动三维肝脏分割方法 |
CN105898278A (zh) * | 2016-05-26 | 2016-08-24 | 杭州电子科技大学 | 一种基于双目多维感知特性的立体视频显著性检测方法 |
CN106203430A (zh) * | 2016-07-07 | 2016-12-07 | 北京航空航天大学 | 一种基于前景聚集度和背景先验的显著性物体检测方法 |
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US10008004B1 (en) | 2018-06-26 |
US20180182118A1 (en) | 2018-06-28 |
CN106651853A (zh) | 2017-05-10 |
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Application publication date: 20170510 Assignee: Luoyang Tiangang Trading Co.,Ltd. Assignor: Beijing University of Technology Contract record no.: X2024980000143 Denomination of invention: A method for establishing a 3D saliency model based on prior knowledge and depth weights Granted publication date: 20191018 License type: Common License Record date: 20240104 Application publication date: 20170510 Assignee: Henan zhuodoo Information Technology Co.,Ltd. Assignor: Beijing University of Technology Contract record no.: X2024980000138 Denomination of invention: A method for establishing a 3D saliency model based on prior knowledge and depth weights Granted publication date: 20191018 License type: Common License Record date: 20240104 Application publication date: 20170510 Assignee: Luoyang Lexiang Network Technology Co.,Ltd. Assignor: Beijing University of Technology Contract record no.: X2024980000083 Denomination of invention: A method for establishing a 3D saliency model based on prior knowledge and depth weights Granted publication date: 20191018 License type: Common License Record date: 20240104 |
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