henon-jiami

说明：  实现图像的清晰分割，效果明显,基于图像灰度-梯度构造的二维直方图，在此基础上计算目标与背景的二维Renyi熵(It can handle more types of images and get more accurate shape of the image edgee that pixels gradient information in combination with parameter of Renyi entropy which is adjustable)

说明：  SVM算法的线性分类，图像分类单幅遥感图像特征提取分类(Linear classification of SVM algorithm, image classification, feature extraction and classification of single remote sensing image)

基于数学形态变换的骨骼CT边缘提取 Canny算子(Transformation of bone based on mathematical morphology edge detection Canny operator CT)

这是我的课程设计作业，作用是测试车流量，对车辆进行定位分割，已验证成功内附测试视频(This is my course design work, the role is to test the traffic flow, the vehicle positioning and segmentation, has been successfully included in the test video)

利用Hough变换检测抛物线的算法。利用标准Hough变换从二值（黑白）图像中检测抛物线。(The use of parabolic Hough transform detection algorithms. Standard Hough transform from the binary (black and white) image in the detection of a parabola.)

说明：  合成孔径雷达点目标仿真，线性变标算法（CSA），注释完整，可以直接运行，信号模型参考书目《合成孔径雷达成像算法与实现》。(Synthetic aperture radar point target simulation, linear scaling algorithm (CSA), complete annotations, can be run directly, the signal model reference bibliography "Synthetic aperture radar imaging algorithm and implementation".)

标准Hough检测圆，在数字图像中检测圆形目标，需要先边缘提取，在二值图像中检测圆。(circle detection using standard hough transform )

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)

基于matlab的图像小波融合测试程序，选取四副位图文件为对比(Matlab wavelet-based image fusion test program)

Canny Edge Detector to detect sharp edges in an image