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Description: 采用一种快速收敛变步长LMS（Least mean square ） 自适应最小均方算法matlab源程序，其中算法所做的工作是用FIR 滤波器的预测系统，对IIR系统进行预测，如果阶数越高越能逼近被预测系统。-Using a fast convergence of variable step size LMS (Least mean square) adaptive least mean square algorithm matlab source, one of algorithm is the work done by FIR prediction filter systems, prediction of IIR systems, if the higher order the more it can be prediction system approximation.
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Author: 杨思科 |
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Description: 这是一种快速的mean-shift算法，他是对于原有mean-shift算法的一种改进，希望能够帮助学习它的同学-This is a fast mean-shift algorithm, he is to the original mean-shift algorithm is an improvement, hoping to help the students learn it
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Size: 4096 |
Author: 杨雨 |
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Description: A paper on Algorithm for Tracking of Fast Motion Objects with Adaptive Mean Shift
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Author: Vidsocio |
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Description: ：Mean—shift算法是一种非参数密度估计算法，可以实现快速的最优匹配，在目标的实时跟踪领域起着非常
重要的作用。为了有效的将Mean—shift算法应用到灰度图像中，采用了以方向直方图建立目标模型的策略，提出了在灰
度图像中以Mean—shift为核心的目标跟踪算法。实验结果表明，该算法具有不受光照条件影响的优点，在低对比度的情
况下仍能实现稳定、实时的跟踪目标。-: Mean-shift algorithm is a nonparametric density estimation algorithm, the optimal matching can be fast, real-time tracking fields in the target plays an important role. In order to effectively Mean-shift algorithm will be applied to the gray image, the histogram used to establish the direction of the strategy target model is proposed in the gray image to Mean-shift tracking algorithm as the core. Experimental results show that the algorithm has the advantage of not lighting conditions, in the case of low contrast can still achieve stability, real-time tracking of targets.
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Size: 310272 |
Author: ridge |
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Description: 是跟踪一个运动的篮球。采用的是Kalman滤波器与基于均值平移的目标跟踪算法相融合的扩展算法，为了跟踪快速运动的目标，首先对目标运动模型进行建模，并运用Kalman滤波器对目标在下一帧中的状态进行预测，并将此预测值作为均值平移算法搜索目标的起始点。-Is tracking a movement of the basketball. Kalman filter is used in the target mean shift based tracking algorithm extension of the integration algorithm, in order to track the fast-moving target, the first model of the target motion model and use Kalman filter to the target in the next frame of the state prediction, and the predictive value of the mean shift algorithm as the starting point of the search target.
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Size: 697344 |
Author: zouyi |
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Description: 本文提出了一种适用于低轨卫异扩频通信系统的多普勒频移快捕方法.该方法基于数字匹配滤波器及自动频率控制环路_利用数字匹配滤波器的囚码快捕特性及其输出主相关峰值对频偏的敏感性，缩短多普勒频移的
捕获时间.论文首先推导了多普勒频移的平均捕获时间表达式，然后根据数值分析结果得到了准最佳捕获判决策略，最后通过比较验证了该快捕方法相对于传统串行捕获方法在性能上的提高.
-This paper presents a low-orbit health for different spread spectrum communication system fast catching the Doppler shift method, which based on digital matched filter and automatic frequency control loop _ prisoners using digital matched filter code features rapid acquisition and the output of the main correlation peak frequency deviation of the sensitivity of the Doppler frequency shift to shorten acquisition time. paper first derive the mean Doppler shift acquisition time expression, then the numerical results obtained quasi-optimal capture decision strategies, and finally verified by comparing with the rapid acquisition method to capture the traditional method of serial improvement in performance.
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Author: 李嵩 |
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Description: 自己编写的一种快速均值漂移聚类算法。数据需要自己下载，里面有main函数,改成自己的数据就可以运行。主要思路是先用近邻规则粗分类，在运行均值漂移。-fast mean shif clustering algorithm.
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Author: fhqu |
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Description: ：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多
特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究，
提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一
种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。
为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明，
该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。-: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.
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Size: 2325504 |
Author: 张娜 |
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Description: Mean Shift算法的基本原理，着重研究了Mean Shift的迭代过程和收敛性以及特征空间的结构。通过Mean Shift结构分析，本章提出了一种快速区域合并算法并将其应用于改进的Mean Shift图像分割算法。-The basic principle of the Mean Shift algorithm, focusing on the Mean Shift iteration process and convergence and the structure of the feature space. Mean Shift structural analysis, this chapter proposes a fast region merging algorithm and apply the Improved Mean Shift image segmentation algorithm.
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Size: 1310720 |
Author: waiwai |
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Description: 均值漂移算法 用于图像分割，快速高效，非常好-Mean shift algorithm for image segmentation, fast and efficient, very good
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Size: 1810432 |
Author: 曹庙根 |
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Description: 文章介绍了mean-shift算法运动矢量相结合的一种解决运动目标过快而是Ms算法丢失目标的跟踪算法。-This paper presents a fast moving target but Ms solving algorithm lost target tracking algorithm mean-shift algorithm combines motion vector.
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Author: 雷川 |
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Description: ASMS仅依靠颜色特征的算法而且速度很快，在VOT2015是20名, 是VOT2015官方推荐的实时算法，平均帧率125FPS。在VOT2016是32名，整体属于中等水平。在经典mean-shift框架下加入了尺度估计，经典颜色直方图特征，加入了两个先验(尺度不剧变+可能偏最大)作为正则项，和反向尺度一致性检查。在相关滤波和深度学习盛行的年代，还能看到mean-shift打榜还有如此高的性价比实在不容易。环境：WIN8.1 64位 +Visual Studio 2015 +OpenCV 3.3.0(ASMS relies only on the color feature algorithm and is fast in speed. It is 20 in VOT2015, a real-time algorithm recommended by the VOT2015, with an average frame rate of 125FPS. The VOT2016 is 32, and the whole is at the middle level. In the classical mean-shift framework, scale estimation is added. Classical color histogram features are added to two priors (scale undrastic + possible maximum) as regularization terms and reverse scale consistency check. In the age of correlation filtering and depth learning, it was also not easy to see that mean-shift had such a high cost performance.)
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Size: 7051264 |
Author: betforever |
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