harris-match

对输入的图像帧感兴趣区域进行逆透视投影变换，得到俯视图。(The input of the image frame of interest in the area of the inverse perspective projection transform, get a top view. InversePerspectiveMapping)

说明：  准确检测图像中的高光区域，并对检测出的高光区域的高光进行去除(Detect highlights, remove highlights)

简单的灰度图像的K均值聚类分割，Matlab实现(gray image segmentation using K-means clustering by matlab.)

源码包括了常用的图像处理方法，代码详细，适合初学者(Source code, including the commonly used image processing method, the code in detail, suitable for beginners)

拉普拉斯变换是工程数学中常用的一种积分变换，又名拉氏转换。拉氏变换是一个线性变换，可将一个有引数实数t（t≥ 0）的函数转换为一个引数为复数s的函数。(Laplace transform is a commonly used engineering mathematics integral transformation, also known as Laplace Transform. Laplace transform is a linear transform, argument can be a real number t (t ≥ 0) is converted to a function of a complex number s argument of the function.)

关于深度玻尔兹曼机的工具箱，可以进行图像的分类和识别。(About Deep Boltzmann Machine Toolbox,which you can use for image classification and identification.)

飞机弱小目标检测，利用固定权重，对背景进行全图重建，再利用残差图像，获得小目标的具体方位。(Aircraft small target detection, the use of fixed weights to rebuild the whole map background, and then use the residual image, obtain the specific location of small targets)

说明：  噪声估计法，先定位噪声点，再实行去噪处理(Noise estimation method, first locate the noise points, then implement denoising.)

基于地物分类真值的高光谱遥感图像分类精度评价(Accuracy uation of remote sensing image classification)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)