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在图像处理中经常涉及分类，多次有用到非参数估计。本程序用Parzen窗来估计，用随机高斯函数来作计策信号.-in image processing involves classification, the many useful to the non - parametric estimation. The procedure used to estimate Parzen window, using random Gaussian function to signal for the ploy
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采用非参数背景建模，MFC，对运动目标进行检测，有很高的参考价值-Using non-parametric background modeling, MFC, the moving target detection, a high reference value
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实验目的：
研究上课所讲谱分析方法，利用实验验证书中的结论，掌握各种谱分析方法，学会实验设计和实验结果分析。
实验内容：
所应用到的谱分析方法，包括：
1） 非参数化方法：周期图（直接法）、BT法（间接法），Welch平均周期图法
2） 参数化方法： RELAX、Capon
3） 空间谱估计：常见的DOA方法（Capon）
-Experimental Objective: To study methods of spectral analysis class talk
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基于核密度估计的背景减法算法，拥有命令行界面的C++源代码。-kernel density estimation based background subtraction algorithm [1] with a command line interface. this algorithm is a somewhat improved version of [2].
the kmovingobjdetector class within the project is originally w
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基于非参数视觉模型的特征图matlab工具-Matlab tools for Saliency Estimation using a non-parametric vision model
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语音信号处理中关于概率密度的估计，分为参数法和非参数法两类。在此基础上用MATLAB做出了仿真。-Speech signal processing on the estimates of the probability density is divided into two types of parametric method and non-parametric method. On this basis, to make the simulation using MATLAB.
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非参数谱估计
x(𝑛 )=sin (0.1𝜋 𝑛 +𝜑 _1 )+0.5 sin (0.6𝜋 𝑛 +𝜑 _2 )+0.5 sin ((0.65𝜋 𝑛 +𝜑 _3 )+0.25 sin ((0.8𝜋 𝑛 +𝜑 _4 )+𝑣 (𝑛 )))
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Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in
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MeanShift算法是一种无参概率密度估计法，算法利用像素特征点概率密度函数的梯度推导而得， MeanShift算法通过迭代运算收敛于概率密度函数的局部最大值，实现目标定位和跟踪，也能对可变形状目标实时跟踪，对目标的变形，旋转等运动也有较强的鲁棒性。MeanShift算法是一种自动迭代跟踪算法，由 MeanShift补偿向量不断沿着密度函数的梯度方向移动。在一定条件下，MeanShift算法能收敛到局部最优点，从而实现对运动体准确地定位。-MeanShift algorithm is a no
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The estimation of the homography between two views is a key step in many applications involving multiple view geometry. The homography exists between two views between projections of points on a 3D plane. A homography exists also between projections
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