中国科学院机构知识库网格系统: 检索

Prompt Frequency Stabilization of Ultra-Stable Laser via Improved Mean Shift Algorithm 期刊论文 OAI收割
ELECTRONICS, 2022, 卷号: 11, 期号: 9, 页码: 10
作者: Fan, Le; Jiao, Dongdong; Liu, Jun; Chen, Long; Xu, Guanjun

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收藏 | 浏览/下载：38/0 | 提交时间：2022/08/15

auto-locking ultra-stable laser mean shift algorithm scanning transmission signal

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing 期刊论文 OAI收割
IEEE ACCESS, 2020, 卷号: 8, 页码: 5777-5786
作者: Zhang, Shunchao; Wang, Yonghua; Wan, Pin; Zhuang, Jiawei; Zhang, Yongwei

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收藏 | 浏览/下载：26/0 | 提交时间：2020/06/02

Cognitive radio robust cooperative spectrum sensing sensing data fusion K-medoids clustering algorithm Mean-shift clustering algorithm

Dynamics of a mean-shift-like algorithm and its applications on clustering 期刊论文 OAI收割
INFORMATION PROCESSING LETTERS, 2013, 卷号: 113, 期号: 1-2, 页码: 8-16
作者: Liu, Yiguang; Li, Stan Z.; Wu, Wei; Huang, Ronggang

收藏 | 浏览/下载：32/0 | 提交时间：2015/09/18

Design of algorithms Mean-shift algorithm Stability Exponential convergence Clustering

Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields 期刊论文 OAI收割
PATTERN RECOGNITION LETTERS, 2011, 卷号: 32, 期号: 7, 页码: 1036-1043
作者: Lin, Lei; Garcia-Lorenzo, Daniel; Li, Chong; Jiang, Tianzi; Barillot, Christian

收藏 | 浏览/下载：31/0 | 提交时间：2015/08/12

MRI segmentation Markov random field Adaptive mean shift Pixon-representation EM algorithm

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：57/0 | 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).

A medical tracking system for contrast media 会议论文 OAI收割
Life System Modeling and Intelligent Computing. International Conference on Life System Modeling and Simulation, LSMS 2010 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010, Wuxi, China, September 17-20, 2010
作者: Chuan Dai; Wang ZL(王哲龙); Zhao HY(赵红宇)

收藏 | 浏览/下载：19/0 | 提交时间：2012/06/06

Tracking Visual tracking Color histogram Mean shift algorithm

Extraction of image semantic features with spatial-range mean shift clustering algorithm 会议论文 OAI收割
2010 IEEE 10th International Conference on Signal Processing, ICSP2010, Beijing, China, October 24-28, 2010
作者: Wang MY(王孟月); Zhang CL(张常麟); Song Y(宋彦)

收藏 | 浏览/下载：17/0 | 提交时间：2017/03/14

Bag-of-visual Words mean shift FP-growth algorithm

Adaptive deformation estimation of moving target by weight image analysis (EI CONFERENCE) 会议论文 OAI收割
2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, May 21, 2010 - May 24, 2010, Wuhan, China
Bai X.-G.; Dai M.

收藏 | 浏览/下载：27/0 | 提交时间：2013/03/25

An algorithm based on weight image analysis is proposed for adaptive deformation estimation of moving target in mean-shift tracking method. At the first we get the weight image from the target candidate region. Then we analyze the differences between the object and background. According to that the area estimation of the target can be converted into the image segmentation task. To realize the adaptive segmentation and estimation we define the threshold as the maximum variance between object and background. Combining the estimated area and covariance matrix we can estimate the width height and orientation of the object. The experimental results on three representative video sequences validate its robustness to the deformable estimation of the targets. 2010 IEEE.

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：65/0 | 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.

Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割
2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China
作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.

收藏 | 浏览/下载：26/0 | 提交时间：2013/03/25

real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.