中国科学院机构知识库网格系统: 检索

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	Single Space Object Image Super Resolution Reconstructing Using Convolutional Networks in Wavelet Transform Domain 会议论文 OAI收割 Chengdu, China, 2020-05-08 作者: Feng, Xubin; Su, Xiuqin; Xu, Zhengpu; Xie, Meilin; Liu, Peng \| 收藏 \| 浏览/下载：48/0 \| 提交时间：2020/07/21 component convolutional neural network wavelet transform space object image
	Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences 期刊论文 OAI收割 IEEE ACCESS, 2020, 卷号: 8, 页码: 150864-150877 作者: Xi, Jiangbo; Xiang, Yaobing; Ersoy, Okan K.; Cong, Ming; Wei, Xin \| 收藏 \| 浏览/下载：46/0 \| 提交时间：2020/10/23 Space debris Feature extraction Machine learning Signal to noise ratio Object detection Image sequences Optical imaging Space debris detection background estimation candidate region extraction deep learning
	Vehicular Daytime High-resolution Imaging System Based on Phase-diversity Technology 期刊论文 OAI收割 Guangzi Xuebao/Acta Photonica Sinica, 2019, 卷号: 48, 期号: 3 作者: M.Ming; T.Chen; T.-S.Xu \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2020/08/24 Image resolution,Diffraction,Imaging systems,Infrared devices,Infrared radiation,Object detection,Optical design,Optical telescopes,Phase measurement,Space telescopes,Wavefronts
	Algorithms and applications for detecting faint space debris in GEO 期刊论文 OAI收割 ACTA ASTRONAUTICA, 2015, 卷号: 110, 页码: 9-17 作者: Sun, Rong-yu; Zhan, Jin-wei; Zhao, Chang-yin; Zhang, Xiao-xiang 收藏 \| 浏览/下载：35/0 \| 提交时间：2015/12/03 Space debris Image processing Object detection Catalogue
	A hybrid image segmentation algorithm for the high spatial resolution remote sensing image 会议论文 OAI收割 34th Asian Conference on Remote Sensing 2013, ACRS 2013,, Bali, Indonesia, October 20, 2013 - October 24,2013 Wang, Ke; Gu, Xingfa; Yu, Tao; Meng, Qingyan; Xu, Hui; Liu, Shuhan 收藏 \| 浏览/下载：21/0 \| 提交时间：2014/12/07 Image classification Algorithms Image reconstruction Image segmentation Object recognition Remote sensing Space optics
	Optical system design with high resolution and large field of view for the remote sensor (EI CONFERENCE) 会议论文 OAI收割 Chang J.; Weng Z.-C.; Wang Y.-T.; Cheng D.-W.; Jiang H.-L. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 In this paper we are presenting a design method and its results for a space optical system with high resolution and wide field of view. This optical system can be used both in infrared and visible configurations. The designing of this system is based on an on-axis three-mirror anastigmatic (TMA) system. Here the on-axis concept allows wide field of view (FOV) enabling a diversity of designs available for the Multi-Object Spectrometer instruments optimized for low scattered and low emissive light. The available FOVs are upto 1 in both spectrum ranges whereas the available aperture range is F/15 - F/10. The final optical system is a three-mirror telescope with two on-axis and one off-axis segment and its resolution is 0.3m or even lower. The distinguished feature of this design is that it maintains diffraction-limited image at wide wavelengths. The technological developments in the field of computer generated shaping of large-sized optical surface details with diffraction-limited imagery have opened new avenues towards the designing techniques. Such techniques permit us to expand these technological opportunities to fabricate the aspherical off-axis mirrors for a complex configuration.
	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：38/0 \| 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