medcalinhance

以SIFT特征为目标输入，LDAHash方法进行降维实现目标识别(SIFT features input LDAHash method to reduce the dimensionality of the target recognition)

说明：  使用vivado HLS工具实现的直方图均衡化(EqualizeHist with vivado HLS)

说明：  文献 "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" 的参考代码 用Lp正则化方法做盲复原的代码 demo_text_deblurring 是主函数(refer to "Deblurring Text Images via L0-Regularized Intensity and Gradient Prior" main function: demo_text_deblurring)

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)

说明：  VC++.NET车牌识别、字符分割与归一化处理，图像对象BMP格式，程序将读取车牌的图像信息，归一化图像大小，动态分配存储图片的像素信息的二维数组，找到图像各列，从左列开始,垂直扫描，实施图片局部分割等，有兴趣编写车牌号识别的可参考对比下。(VC ++. NET license plate recognition, character segmentation and normalization processing, image object BMP format, the program will read the license plate image information, normalize the image size, dynamically allocate a two-dimensional array of pixel information to store the picture, and find the image columns , Starting from the left column, vertical scanning, partial image segmentation, etc. If you are interested in writing the license plate number recognition, please refer to the comparison.)

对图像进行大津法阈值分割，以及图像的分块。(Image Otsu threshold segmentation, as well as block images.)

实现图像融合的一些基本代码 实现了梯度金字塔拉普拉斯金字塔算法的图像融合 (matlab This procedure based on the implementation of the FSD pyramid image fusion algorithm, the effect was not bad, you can reference.)

实现对图像的经验模态分解，得到经验模态分解后的第一层IMF分量，反映了图像的主要信息，去除冗余信息，为识别提供了基础。(Empirical mode decomposition of image, obtain the empirical mode decomposition of the first layer of IMF component, reflected the main information of the image, to remove redundant information, provide the basis for identification. )

人脸跟踪的一些主流算法的matlab实现(face tracking algorithm for the mainstream to achieve the Matlab)

一种二维EMD算法，作者是Anna Linderhed，该学者博士研究课题为二维EMD和小波变换，她的二维EMD算法与Nenus或其他学者的算法有较大的不同。(A two-dimensional EMD algorithm, the author is Anna Linderhed, her Ph.D. research topic is bidimensional EMD and wavelet transform. Her two-dimensional EMD algorithm is quite different from that of Nenus and other scholars.)