利用阈值法分割图像的matlab程序和实验报告，这是本科学习图象处理时的作业，供初学者参考...

在vc++中，使用单文档程序，对数字图像进行拉普拉斯变换。供有兴趣的朋友学习-In vc++, The use of single-document procedures Laplace transform digital images. For interested people in learning

关于OpenCV的帮助文档，有很好的借鉴作用。-OpenCV on the help of documents, has a very good reference.

image processing and affine transformation

可以对D-INSAR数据的沉陷应变进行有效计算-On D-INSAR data for effective calculation of subsidence strain

霍夫变换的 m文件 ，希望那个可以供大家是使用。-Hough transform of the m file, in the hope that can be for everyone is to use.

Visual c++ MATLAB图像处理与识别 书盘源码上传0000000004－－第4章-Visual c MATLAB image processing and recognition site source book Upload 0000000004-- Chapter 4

计算坐标是否落在给定三顶点的三角形内程序 （做多边形图形填充时的副产品）-calculated coordinates fall on whether to set three vertices within the triangle (polygon graphics do when filling a byproduct)

（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 -(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.

基于活动轮廓模型的图像分割算法，具体算法细节，可以见压缩包中的论文-Based on Active Contour Model of image segmentation algorithm, the details of the specific algorithm, you can see the archive of papers

KPCA主要在图像去噪声方面有应用。此外还可以进行特征提取，降维使用 -KPCA major noise in the image to have the application. In addition can also be used for feature extraction, dimensionality reduction using