(标准)Hough变换检测圆

基于互信息的刚性配准，利用了Powell算法和黄金分割算法，效果还不错(Based on Mutual Information rigid registration, Powell algorithm and the use of the golden section algorithm, the results were good)

对图像分块,计算每个块的均值和方差,并给出了结果的图象化表示,易于观察(Image sub-blocks, each block calculated the mean and variance, and gives the results of the image that is easy to observe)

画图源程序-图像数据显示，可以对数据进行时间轴的曲线显示，也可以是空间轴的多维显示 (Paint source- the image data, the data can be curve shows the time axis, it can be multi-dimensional display space axis)

说明：  雷达遥感图像的小波去噪程序，可有效去除噪声(radar remote sensing image wavelet denoising procedures, which can effectively remove noise)

这是我本科的毕业设计做的关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。(This is my undergraduate graduation design done on the medical images based on the shape of the image retrieval. Pretreatment with wavelet denoising, feature selection invariant moment.)

能够实现对生物芯片的识别和图像处理，识别有效芯片区域，读取阴性和阳性点分布和数量。(It can realize the recognition and image processing of biochip, identify the effective chip area, read the distribution and number of negative and positive points.)

用粒子滤波器实现的多运动员跟踪，包含了data文件(Using Particle Filters athletes realize the multi-tracking, contains a data file)

利用idl语言实现图像的数学形态学开闭重建运算，比提取梯度图像。(Idl language use mathematical morphological opening and closing image reconstruction operations, compared to extract gradient image.)

对图像进行模糊处理前的第一步设计操作，然后进行加强滤波处理(Fuzzy image processing operation prior to the first step in the design and then proceed to strengthen the filter processing)

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)