中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共9条，第1-9条

帮助

条数/页：排序方式：
	Object Affinity Learning: Towards Annotation-Free Instance Segmentation 期刊论文 OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 11, 页码: 13959-13973 作者: Wang, Yuqi; Chen, Yuntao; Zhang, Zhaoxiang \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2023/12/21 Videos Motion segmentation Visualization Three-dimensional displays Task analysis Object detection Geometry Object affinity learning geometric information annotation-free instance segmentation
	Efficient 3D object recognition via geometric information preservation 期刊论文 OAI收割 Pattern Recognition, 2019, 卷号: 92, 页码: 135-145 作者: Yang, Chenguang; Liu HS(刘洪森); Cong Y(丛杨); Tang YD(唐延东) \| 收藏 \| 浏览/下载：58/0 \| 提交时间：2019/04/13 Stacked 3D feature encoder 3D object recognition 6-DOF pose estimation Geometric information preservation
	Dual Graph Regularized Discriminative Multi-task Tracker 期刊论文 OAI收割 IEEE Transactions on Multimedia, 2018, 页码: 1-14 作者: Tang YD(唐延东); Fan BJ(范保杰); Cong Y(丛杨) \| 收藏 \| 浏览/下载：48/0 \| 提交时间：2018/02/24 Multi-task tracker Discriminative low rank learning Geometric structure information Graph regularization, Collaborate metric
	Geometry-based propagation of temporal constraints 期刊论文 OAI收割 INFORMATION SYSTEMS FRONTIERS, 2017, 卷号: 19, 期号: 4, 页码: 855-868 作者: Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Xu, Lida; He, Wu \| 收藏 \| 浏览/下载：33/0 \| 提交时间：2019/12/12 Internet of Things (IoT) Information integration Temporal constraints 2-dimensional space Geometric method
	A symmetric geometric measure and the dynamics of quantum discord 期刊论文 OAI收割 CHINESE PHYSICS B, 2013, 卷号: 22, 期号: 4 作者: Jiang FengJian; Lu HaiJiang; Yan XinHu; Shi MingJun \| 收藏 \| 浏览/下载：17/0 \| 提交时间：2021/12/13 ENTANGLEMENT INFORMATION STATES POWER quantum correlation geometric measure
	Automatic bridge extraction for optical images (EI CONFERENCE) 会议论文 OAI收割 6th International Conference on Image and Graphics, ICIG 2011, August 12, 2011 - August 15, 2011, Hefei, Anhui, China Gu D.-Y.; Zhu C.-F.; Shen H.; Hu J.-Z.; Chang H.-X. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 This paper describes a novel hierarchy algorithm for extracting bridges over water in optical images. To reduce the omission of bridges by searching the edge we extract the river regions which the bridges are included in. Firstly we segment the optical image to get the coarse water bodies using iterative threshold eliminate the noise regions and add the missing regions based on k-means clustering with texture information and spatial coherence. Then the blanks are connected based on shape features and candidate bridge regions are segmented from river regions. Finally the bridges are verified by geometric information and the ubiety between bridges and river. The results show that this approach is efficient and effective for extracting bridges in satellite image from Google Earth and in aerial optical images acquired by unmanned aerial vehicle. 2011 IEEE.
	A uncertainty analysis method based on geometric structure of geographic feature 会议论文 OAI收割 2008 Proceedings of Information Technology and Environmental System Sciences: Itess 2008, Vol 3, Beijing Guo, Jifa; Cui, Weihong; Wei, Fengyuan 收藏 \| 浏览/下载：24/0 \| 提交时间：2014/12/07 geographic information system (GIS) spatial data uncertainty G-Band geometric structure geographic feature
	Data preprocessing of the exterior field of vision assembling photogrammetric camera (EI CONFERENCE) 会议论文 OAI收割 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, July 8, 2007 - July 12, 2007, Chengdu, China 作者: Li J.; Li J.; Li J.; Liu J.; Liu J. 收藏 \| 浏览/下载：82/0 \| 提交时间：2013/03/25 Transmit array photogrammetric camera can obtain image which is high geometric fidelity and high photogrammetric quality. However the single chip array CCD image sensor camera can't meet the need of measuring precision and photogrammetric covering area. In order to obtain large numbers of information and extensive photogrammetric covering area we must increase field of vision angle and improve photogrammetric covering area. And all these objects can be realized by exterior field of vision assembling photogrammetric camera. Two side work must be done before images which obtained by exterior field of vision assembling photogrammetric camera be used in photogrammetry. First all assembling camera focal plane need be converted to a benchmark coordinate focal plane to realize camera digital assembling. Second images must be re-sampled and processing. Because of coordinate conversion two images from different assembling cameras can be established function relation which a pixel of image from a camera is corresponding to a pixel of another image from different camera. But through this conversion some pixels maybe extrusion together and other pixels separate on an image area. So interpolation direction finding(IDF) is used to obtain these pixels and realize image re-sampling. In this paper the structure of exterior field of vision assembling photogrammetric camera is analyzed and the coordinate conversion method of exterior field of vision assembling photogrammetric camera and image gray re-sampling method also can be discussed. All the works are based to data pretreatment of exterior vision assembling photogrammetric camera.
	A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Yao Z.-J. 收藏 \| 浏览/下载：29/0 \| 提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However 3. applying the standard Hough transform equation to every point of the input image edge 4. according to the local threshold 6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes traditional Hough transform can only detect the lines 2. quantizing the parameter space and extracting a group of maximums according to the global threshold eliminating spurious peaks which are caused by the spreading effects and will improve the precision of tracking. cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately 5. fixing on the endpoints of the segments according to the dynamic clustering rule which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image parameter and line-segment spaces