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	Scale and pattern adaptive local binary pattern for texture classification[Formula presented] 期刊论文 OAI收割 Expert Systems with Applications, 2024, 卷号: 240 作者: Hu, Shiqi; Li, Jie; Fan, Hongcheng; Lan, Shaokun; Pan, Zhibin \| 收藏 \| 浏览/下载：46/0 \| 提交时间：2024/02/07 Local binary pattern (LBP) Texture classification Low dimension Scale and pattern adaptive selection Kirsch operator
	Cloud detection from visual band of satellite image based on variance of fractal dimension 期刊论文 OAI收割 JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2019, 卷号: 30, 期号: 3, 页码: 485 作者: Tian Pingfang; Guang Qiang; Liu Xing \| 收藏 \| 浏览/下载：2/0 \| 提交时间：2023/12/04 cloud detection visual image satellite image variance of local fractal dimension (VLFD)
	An improved LLE-based cluster security approach for nonlinear system fault diagnosis 期刊论文 OAI收割 CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 卷号: 22, 期号: S3, 页码: 5663-5673 作者: \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2017/12/21 Local Linear Embedding Tangent Space Distance Intrinsic Dimension Fault Diagnosis
	Effects of surface roughness on the heat transfer characteristics of water flow through a single granite fracture 期刊论文 OAI收割 COMPUTERS AND GEOTECHNICS, 2016, 卷号: 80, 页码: 312-321 作者: He, Yuanyuan; Hu, Shaobin; Li, Xiaochun; Bai, Bing \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2018/06/05 Fracture geometry Surface roughness Fractal dimension Profile waviness Local heat transfer coefficient
	基于词袋模型的图像表示及其在图像分类中的应用学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院大学, 2012 孙涛收藏 \| 浏览/下载：66/0 \| 提交时间：2015/09/02 图像分类图像表示词袋模型特征降维局部特征上下文 Image classification Image representation Bag of Words Feature dimension reduction Local feature context
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：63/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	维数约简中的数据性质研究学位论文 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: 毕华收藏 \| 浏览/下载：67/0 \| 提交时间：2015/09/02 机器学习维数约简局部学习稳健性重采样 Boosting machine learning dimension reduction local learning robust resampling Boosting
	流形学习若干问题研究学位论文 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, 2006 作者: 杨剑收藏 \| 浏览/下载：111/0 \| 提交时间：2015/09/02 流形学习维数约简局部切空间半监督回归 manifold learning dimension reduction local tangent space semi-supervised regression
	Measuring the range of an additive Levy process 期刊论文 OAI收割 ANNALS OF PROBABILITY, 2003, 卷号: 31, 期号: 2, 页码: 1097-1141 作者: Khoshnevisan, D; Xiao, YM; Zhong, YQ \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2018/07/30 additive Levy processes strictly stable processes capacity energy local times Hausdorff dimension