机构

• 长春光学精密机械与物... [2]
• 青藏高原研究所 [1]
• 计算技术研究所 [1]
• 数学与系统科学研究院 [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]
• 更多

采集方式

• OAI收割 [7]

内容类型

• 期刊论文 [5]
• 会议论文 [2]

发表日期

• 2022 [1]
• 2019 [2]
• 2010 [1]
• 2009 [1]
• 2006 [2]

学科主题

• 地球科学::地球化学 [1]

筛选

浏览/检索结果: 共7条，第1-7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 Not All Samples are Trustworthy: Towards Deep Robust SVP Prediction 期刊论文  OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 卷号: 44, 期号: 6, 页码: 3154-3169 作者:  Xu, Qianqian;  Yang, Zhiyong;  Jiang, Yangbangyan;  Cao, Xiaochun;  Yao, Yuan   |  收藏  |  浏览/下载：36/0  |  提交时间：2022/12/07 Noise measurement  Annotations  Task analysis  Predictive models  Robustness  Visualization  Training  Subjective visual property (SVP)  robustness  outlier detection  probabilistic model   Ergodicity for a class of semilinear stochastic partial differential equations 期刊论文  OAI收割 MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2019, 页码: 20 作者:  Dong, Zhao;  Zhang, Rangrang   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/05/24 invariant measures  irreducibility  semilinear partial differential equations  space-time white noise  strong Feller property   Property Investigation on the Additive White Gaussian Noise After Sub-Nyquist Sampling 期刊论文  OAI收割 IEEE ACCESS, 2019, 卷号: 7, 页码: 122820-122826 作者:  Gong TR(宫铁瑞);  Liu ZF(刘志峰);  Xie C(谢闯)   |  收藏  |  浏览/下载：41/0  |  提交时间：2019/10/10 Noise property  noise folding factor  sub-Nyquist sampling   Cr image enhancement based on human visual characteristics (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer Design and Applications, ICCDA 2010, June 25, 2010 - June 27, 2010, Qinhuangdao, Hebei, China Zhang M.-H.; Zhang Y.-Y. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 The characteristic of digital CR medicine radiation image has wide dynamic range  abundant details and bad contrast  so it is necessary to enhance CR image to the need of doctor diagnosis. But the general enhancement algorithms don't consider human visual characteristics  so it puts forward CR medicine image adaptive enhancement algorithm combining the human visual property  which is more sensitive to smooth area noise compared with detail area noise  and makes image edge detail enhancement great in detail area  and detail enhancement little in smooth area  in which factor K is based on space change of image domain  accordingly gaining non-linear enhancement edge details of CR image. Experiment results demonstrate that the algorithm enhances CR image detail and CR image enhanced has good visual effect  so the method is fit for edge detail enhancement of CR medicine radiation image. 2010 IEEE.   Analysis of geophone properties effects for land seismic data 期刊论文  OAI收割 APPLIED GEOPHYSICS, 2009, 卷号: 6, 期号: 1, 页码: 93-101 Li GL (李桂林); Chen; G (Chen Gao); Zhong; JY (Zhong Junyi) 收藏  |  浏览/下载：21/0  |  提交时间：2010/04/21 Property index  geophone  resolution  natural frequency  signal/noise ratio   Chord-based image reconstruction in cone-beam CT with a curved detector 期刊论文  OAI收割 MEDICAL PHYSICS, 2006, 卷号: 33, 期号: 10, 页码: 3743-3757 作者:  Zuo, Nianming;  Xia, Dan;  Zou, Yu;  Jiang, Tianzi;  Pan, Xiao-Chuan 收藏  |  浏览/下载：21/0  |  提交时间：2015/11/08 computed tomography  cone-beam  curved detector  chords  image reconstruction  noise property   A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25 In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.