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构建了一种新的Harris多尺度角点检测算法,角点的图像配准算法的配准精度(Construction of a new multi-scale Harris corner detection algorithm, corner of the image registration algorithm for registration accuracy)

这种方法可以用来实现对SAR图像的多普勒中心进行估计(This method can be used to realize on the SAR images to estimate the Doppler centroid)

说明：  1、正弦函数 f (t) = K sin(wt +a) 2、矩形脉冲函数 f (t) = u(t) - u(t - t0 ) 3、抽样函数 4、单边指数函数 f (t) = Ke -t 5、已知信号 f1(t) = u(t + 2) - u(t - 2)， f 2 (t) = cos(2pt) ，用 MATLAB 绘制 f1(t) + f 2 (t) 和 f1(t)′ f 2 (t)的波形。(such as : Sine function f (t) = K sin(wt +a) Rectangular pulse function f (t) = u(t)-u(t-t0) Sampling function One-sided exponential function f (t) = Ke -t Known signal f1(t) = u(t + 2)-u(t-2), f 2 (t) = cos(2pt), draw with MATLAB The waveforms of f1(t) + f 2 (t) and f1(t)*f 2 (t))

Bezier curve creation and plotting with fortran, greek .docs

Space structure with static analysis % elastic beam element space structure with static analysis, elastic beam element

利用matlab对彩色图像进行分割处理，图像针对bmp jpg pcn格式(color image segment)

影像融合评价程序，计算标准偏差，平均梯度，相关系数，光谱扭曲度等融合指标。(Image fusion evaluation, calculation of standard deviation, average gradient, correlation coefficient, spectral distortions, such as integration targets.)

SIFT，SURF，MSER函数库,包括MATLAB和C++源程序(SIFT SURF MSER function)

支持向量机在小波包去噪方法中的应用，小波包分解和svm相结合(Support vector machines in wavelet packet denoising of wavelet packet decomposition and combination svm)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)