机构

• 自动化研究所 [5]
• 长春光学精密机械与物... [4]
• 地球环境研究所 [2]
• 地质与地球物理研究所 [1]
• 西安光学精密机械研究... [1]

采集方式

• OAI收割 [13]

内容类型

• 期刊论文 [7]
• 会议论文 [4]
• 学位论文 [2]

发表日期

• 2024 [1]
• 2018 [2]
• 2014 [1]
• 2012 [3]
• 2009 [1]
• 2008 [3]
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浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 CFNet: Conditional filter learning with dynamic noise estimation for real image denoising 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2024, 卷号: 284, 页码: 12 作者:  Zuo, Yifan;  Yao, Wenhao;  Zeng, Yifeng;  Xie, Jiacheng;  Fang, Yuming   |  收藏  |  浏览/下载：50/0  |  提交时间：2024/03/26 Image denoising  Noise estimation  Conditional filter  Affine transform   Hyperspectral image classification based on joint spectrumof spatial space and spectral space 期刊论文  OAI收割 Multimed Tools Appl, 2018, 期号: 2018 作者:  Hu, BL(Hu, Bingliang);  Zheng, X(Zheng, Xi);  Zhang, Xiaorong;  Pan, ZB(Pan, Zhibin);  Zhang, XR(Zhang, Xiaorong)   |  收藏  |  浏览/下载：44/0  |  提交时间：2018/09/29 Classification  Hyperspectral Imagery  Spectral-spatial Fusion  Affine Transform  Feature Extraction  Probabilistic Fusion   Hyperspectral image classification based on joint spectrum of spatial space and spectral space 期刊论文  OAI收割 Multimedia Tools and Applications, 2018, 卷号: 77, 期号: 2018, 页码: 1-19 作者:  Zhang, XR(Zhang, Xiaorong);  Pan, ZB(Pan, Zhibin);  Lu, XQ(Lu, Xiaoqiang);  Hu, BL(Hu,Bingliang);  Zheng, X(Zheng, Xi)   |  收藏  |  浏览/下载：51/0  |  提交时间：2018/09/29 Classification  Hyperspectral Imagery  Spectral-spatial Fusion  Affine Transform  Feature Extraction  Probabilistic Fusion   Corner detection using Gabor filters 期刊论文  OAI收割 iet image processing, 2014, 卷号: 8, 期号: 11, 页码: 639-646 作者:  Zhang, Wei-Chuan;  Wang, Fu-Ping;  Zhu, Lei;  Zhou, Zuo-Feng 收藏  |  浏览/下载：51/0  |  提交时间：2015/03/19 edge detection  Gabor filters  grey systems  affine transforms  Gabor filter  contour-based corner detection  magnitude response  edge contour shape analysis  local curvature maxima points  planar curve  corresponding grey-variation information  Canny edge detector  normalised magnitude response  magnitude response threshold  angle threshold  detection accuracy  localisation accuracy  afflne transform  noise-robustness   A fast target recognition algorithm based on MSA and MSR (EI CONFERENCE) 会议论文  OAI收割 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23, 2012 - August 25, 2012, Xi'an, China 作者:  Wang Y.;  Liu G.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 This paper presents a new fast target recognition algorithm  the proposed method is based on Multi-scale Auto convolution(MSA) and Multi-scale Retinex(MSR). As shown by the comparison with original MSA  it appears that this new technique solves the problem that MSA algorithm is sensitive to illumination and the computational load is significantly reduced to 1/8th of that of the original MSA algorithm  it is also robust to affine transform  light projective transform  noise  thin fog  occlusion and illumination change. the performed experiments show that it has fast searching speed  and can accurately recognize and locate target in real scenes. 2012 IEEE.   Image registration based on Mexican-hat wavelets and pseudo-Zernike moments (EI CONFERENCE) 会议论文  OAI收割 2012 World Automation Congress, WAC 2012, June 24, 2012 - June 28, 2012, Puerto Vallarta, Mexico 作者:  Liu Y.;  Liu Y.;  Liu Y. 收藏  |  浏览/下载：36/0  |  提交时间：2013/03/25 Image registration is a key technique in pattern recognition and image processing  and it is widely used in many application areas such as computer vision  remote sensing  image fusion and object tracking. A method for image registration combining Mexican-hat wavelets and pseudo-Zernike moments is proposed. Firstly  feature points are extracted using scale-interaction Mexican-hat wavelets in the reference image and sensed image respectively. Then  pseudo-Zernike moments are used to match them and classical RANSAC used to eliminate the wrong matches. And then  the well match points are used to estimate the best affine transform parameters by least squares minimization. At last  the sensed image is transformed and resampled to accomplish the image registration. The experiments indicate that the proposed algorithm extracts feature points and matches them exactly and eliminates wrong matched points effectively and achieves nice registration results. 2012 TSI Press.   Blind super-resolution reconstruction algorithm under affine motion model 期刊论文  OAI收割 Pattem Recognition and Aitificial Intelligence,, 2012, 卷号: 25(4), 期号: 2012年04期, 页码: pp 648-655 (EI) 作者:  Zhang, Xue-Song;  Jiang, Jing;  Peng, Si-Long   |  收藏  |  浏览/下载：22/0  |  提交时间：2017/01/13 Affine Transform / blind Super-resolution (Bsr / motion Estimation / blur    图像的匹配扩散研究 学位论文  OAI收割 工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2009 许振辉 收藏  |  浏览/下载：34/0  |  提交时间：2015/09/02 准稠密匹配  扩散  鱼眼图像  仿射变换  邻域传递  quasi-dense matching  propagation  fish-eye image  affine transform  neighborhood-transfer   Seismic data reconstruction with fractal interpolation 期刊论文  OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2008, 卷号: 51, 期号: 4, 页码: 1196-1201 作者:  Li Xin-Fu;  Li Xiao-Fan   |  收藏  |  浏览/下载：12/0  |  提交时间：2018/09/26 iterated function systems (IFS)  affine transform  explicit fractal interpolation  vertical scaling factors  seismic data reconstruction   Mean shift tracking combining SIFT (EI CONFERENCE) 会议论文  OAI收割 2008 9th International Conference on Signal Processing, ICSP 2008, October 26, 2008 - October 29, 2008, Beijing, China 作者:  Xue C. 收藏  |  浏览/下载：63/0  |  提交时间：2013/03/25 A novel visual tracking algorithm to cope with occlusion and scale variation is proposed. This method combines mean shift and SIFT algorithm to track object. SIFT algorithm is invariant to rotation  translation and scale variation. But it is a timeconsuming algorithm. The wasting time is related to image size. So the proposed algorithm first adopts mean shift to initially locate object position  then SIFT operator is used to detect features in object area and model area  lastly  the proposed method matches features in these two areas and calculates the relationship between them using affine transform. According to affine transform parameters  the state of object can be adjusted in time. In order to reduce process time  an improved feature matching algorithm is proposed in this paper. Experiments show that the proposed algorithm deals with occlusion successfully and can adjust object size in time. 2008 IEEE.