中国科学院机构知识库网格系统: 检索

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	LARGE N LIMIT OF THE O(N) LINEAR SIGMA MODEL VIA STOCHASTIC QUANTIZATION 期刊论文 OAI收割 ANNALS OF PROBABILITY, 2022, 卷号: 50, 期号: 1, 页码: 131-202 作者: Shen, Hao; Smith, Scott A.; Zhu, Rongchan; Zhu, Xiangchan \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2022/04/29 O(N) linear sigma model Phi(4) mean-field limit stochastic quantization space-time white noise
	Exact solutions of the generalized two-photon and two-qubit Rabi models 期刊论文 OAI收割 EUROPEAN PHYSICAL JOURNAL D, 2013, 卷号: 67, 期号: 8, 页码: 162 Peng, J; Ren, ZZ; Guo, GJ; Ju, GX; Guo, XY 收藏 \| 浏览/下载：30/0 \| 提交时间：2014/04/25 JAYNES-CUMMINGS MODEL SPACE QUANTIZATION QUANTUM ENTANGLEMENT
	Integrability and solvability of the simplified two-qubit Rabi model 期刊论文 OAI收割 JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2012, 卷号: 45, 期号: 36, 页码: 365302 Peng, J; Ren, ZZ; Guo, GJ; Ju, GX 收藏 \| 浏览/下载：25/0 \| 提交时间：2014/04/25 SPACE QUANTIZATION QUANTUM OPTICS BEHAVIOR
	Building descriptive and discriminative visual codebook for large-scale image applications 期刊论文 OAI收割 MULTIMEDIA TOOLS AND APPLICATIONS, 2011, 卷号: 51, 期号: 2, 页码: 441-477 作者: Tian, Qi; Zhang, Shiliang; Zhou, Wengang; Ji, Rongrong; Ni, Bingbing \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2019/12/16 Visual vocabulary Large-scale image retrieval Image search re-ranking Feature space quantization
	基于梯度下降的分类学习-BN,LVQ,AdaBoost 学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2009 作者: 靳小波收藏 \| 浏览/下载：68/0 \| 提交时间：2015/09/02 分类学习梯度下降参数空间泛函空间贝叶斯网络学习矢量量化 AdaBoost Classification Learning Gradient Descent Parameter Space Functional Space Bayesian Network Learning Vector Quantization AdaBoost
	Space camera imaging gain in-orbit adjusting strategy (EI CONFERENCE) 会议论文 OAI收割 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, October 10, 2009 - October 11, 2009, Changsha, Hunan, China 作者: Wang J.; He X. 收藏 \| 浏览/下载：17/0 \| 提交时间：2013/03/25 A Multi-Step Gain Adjusting Strategy (MSGAS) of space camera was proposed which was used to get higher SNR (Signal-Noise Rate) image. When the space camera working in poor light condition the CCD signal was so weak that it's difficult to get a clear image to reduce the quantization noise and improve the SNR we must amplify the CCD signal first and then quantize. The MSGAS was achieved by adjusting the gain of the CCD (Charge Couple Device) signal processor step by step the upper limit and lower limit were set if the MDN (Mean Digital Number) of a fixed length image data was not between the lower and upper limit the gain was adjusted step by step. In the experiment the upper limit lower limit and the step were set and the result of the experiment showed that MSGAS was robust and SNR was improved from 28 to 39. 2009 IEEE.
	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：27/0 \| 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
	Quantizing the de Sitter space-times 期刊论文 OAI收割 PHYSICS LETTERS B, 2004, 卷号: 602, 期号: 3-4, 页码: 226-230 作者: Xiang, L; Shen, YG 收藏 \| 浏览/下载：15/0 \| 提交时间：2010/11/08 reduced phase space quantization canonical transformation N bound