Tvandpde

可以打开大部分图片格式包括bmp、jpg等，并对其进行图片的基本处理包括翻转、镜像、傅里叶变换、直方图统计及均衡化、锐化、柔化、加椒盐或高斯噪声、中值滤波、Sobel边缘检测、高斯边缘检测、灰度阀值边缘检测、区域生长、旋转、缩放等操作(Can open most image formats including bmp, jpg, etc., and its basic image processing including flip, mirror, Fourier transform, histogram equalization statistics and, sharpen, soften, add salt and pepper or gaussian noise , median filtering, Sobel edge detection, Gaussian edge detection, gray level threshold edge detection, region growing, rotation, scaling and other operations)

图像处理领域最新的K-L变换后求主成分的程序。matlab文件。(image processing latest K-L transform PCA for the procedure. Matlab document.)

（1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 （2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 （3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量； 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)

利用sift算法提取两张图像的特征点，利用ransac去除误匹配，最后将两张图片拼接在一起(Using sift algorithm to extract the two feature points of the image, the use of ransac to remove the wrong match, and finally the two pictures together)

C#截取屏幕源码 可直接截屏、截取当前窗口 实例下载

sample illust back image2 mask sample

极坐标图像转直角坐标图像，相当于球面的展开效果，最好是正方形图像，但是长方形也可以。(POLARTOIM converts polar image to rectangular image)

可自选输入和参数的中值/均值/拉伸滤波，各项参数说明详见.m文件注释(Median/Average/Linear Stretch Filter)

说明：  以实例列举了MATLAB中常用的图像处理函数的代码(Examples are given to illustrate the image processing functions commonly used in MATLAB code)

简单的huffman编解码例子，对于初学者会有很大的帮助。(Simple huffman codec examples, for beginners, there will be a great help.)