demo

用于分割2D、3D图像的可交互graph cut程序，纯matlab实现，不含C++(An interactive graph cut program segmenting 3D images)

分块计算图像的纹理，包括均值，标准偏差和平滑度等(The texture of a image is calculated based on blocks,including mean, standard deviation and smoothness and so on. )

说明：  基于光流法的运动目标检测 也可用于流体力学运动检测(Optical flow method based on the motion detection Motion detection can also be used for fluid dynamics)

说明：  给定受感染红细胞图片，自动判定受感染红细胞数目(Given the picture of infected red blood cells, the number of infected red blood cells can be determined automatically.)

图像检测，目标检测简单来说就是找出图像中的目标并给定目标的位置，一般用矩形框定位出目标(In image detection, target detection is simply to find out the target in the image and give the location of the target. In general, the rectangular box is used to locate the target)

说明：  MIT的人脸数据库，包括人脸和非人练部分。(face database from MIT)

对自适应双边滤波进行图像增强，使图像去噪效果更好(Adaptive bilateral filtering for image enhancement, image denoising effect is better)

图像配准配准结果检验程序,其他人不需帐号就可自由下载此源码 (image registration Registration results of testing procedures, other people can not account free download this source)

这本书提供了充分的代表性逆合成孔径雷达（ISAR）成像，这是一种流行和重要的雷达信号处理工具。本书涵盖了所有可能的ISAR成像方面。这本书提供了一个介绍了专家组的逆问题和合成孔径雷达（SAR）提出的问题之前，信号处理技术和雷达基本相当。如高分辨率SAR的重要概念，脉冲压缩和图像形成连同相关的MATLAB代码。 ISAR成像的基本原理后，这本书提供了相关的MATLAB函数和代码成像ISAR成像的详细程序。 ISAR成像，多个成像技巧和微调，如零填充和窗口程序，以提高图像的质量也被提出。最后，逆合成孔径雷达图像中的各种实际应用，如成像天线平台散射的，是在单独的一章。所有这些算法，MATLAB代码和数字都包括在内。最后一章考虑了在ISAR成像的先进理念和发展趋势。(This book provides a full representation of Inverse Synthetic Aperture Radar (ISAR) imagery, which is a popular and important radar signal processing tool. The book covers all possible aspects of ISAR imaging. The book offers a fair amount of signal processing techniques and radar basics before introducing the inverse problem of ISAR and the forward problem of Synthetic Aperture Radar (SAR). Important concepts of SAR such as resolution, pulse compression and image formation are given together with associated MATLAB codes. After providing the fundamentals for ISAR imaging, the book gives the detailed imaging procedures for ISAR imaging with associated MATLAB functions and codes. To enhance the image quality in ISAR imaging, several imaging tricks and fine-tuning procedures such as zero-padding and windowing are also presented. Finally, various real applications of ISAR imagery, like imaging the antenna-platform scattering, are given in a separate chapter. For all these algorithms, MATLA)

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)