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基于AdaBoost的人脸检测程序代码(是用c++编写的)(AdaBoost-based face detection program code (written with c++))

用hough变换提取直线的C++代码，效果比较好，可以灵活改变可提取直线的条数，适用于提取多条直线。(line detecting using hough tranaformation)

经典矩量法点匹配程序，重推导，注释说明详细(Classical point matching moment method procedure)

图像处理程序中图像处理多运动目标程序，完整程序。下载就能用(Image processing program, image processing of multiple moving targets program, complete the program. Download will be able to use)

ITK的脑室图像分割,可以对脑部的图像用itk进行分割，用VTK来进行显示。(the segration of brain of ITK )

使用大津法选定二值化阈值，并对彩色图像做二值化处理(Otsu binarization threshold is selected, and the color images do binarization processing)

包括图像输入（打开文件、采集、捕捉图像、从剪贴板粘贴、打开扫描软件、灰度化、添加随机噪声、椒盐噪声、去噪声）、图像处理（几何变换、灰度反转、直方图均衡化、直方图统计、图像复原、边缘提取、伪彩色、二值化、图像变换）、图像输出（另存图像、打印输出、复制到剪贴板）(Including image input (open the file, capture, capture images, paste from the clipboard, open the scanning software, graying, adding random noise, Salt and pepper noise, to noise), image processing (geometric transformation, gray inversion, histogram equalization oriented, histogram statistics, image restoration, edge extraction, pseudo-color, binary, image transform), image output (save the image, print out, copy to clipboard))

基于分块的人脸图像超分辨率学习算法实验平台，利用学习到的训练集图像信息对低分辨率图像超分辨率，并可以查看超分辨率后的图像块匹配结果（用鼠标双击结果显示窗口中的实验图像），可以设置实验参数。(Block-based face image super-resolution algorithm for the experimental platform to learn, learning to use the training set of low-resolution image information super-resolution images and can view the image super-resolution block-matching results (double-click with the mouse results display window of the experimental images), the experimental parameters can be set.)

基于加权最小二乘的滤波器。实现应用的例子。(weighted least square)

说明：  傅立叶变换在图像处理中的重要作用： (A) 图像增强与图像去噪 绝大部分噪音都是图像的高频分量，通过低通滤波器来滤除高频噪声；边缘也是图像的高频分量，可以通过添加高频分量来增强原始图像的边缘； (B) 图像分割与边缘检测 提取图像高频分量 (C) 图像特征提取 形状特征：傅里叶描述子 纹理特征：直接通过傅里叶系数来计算纹理特征 其他特征：将提取的特征值进行傅里叶变换来使特征具有平移、伸缩、旋转不变性 (D ) 图像压缩 可以直接通过傅里叶系数来压缩数据；常用的离散余弦变换是傅立叶变换的实变换； (Fourier transform in the important role of image processing: (A) image enhancement and image denoising Most of all images of high frequency noise by low pass filter to filter out high frequency noise edge is the image of the high frequency components can be enhanced by adding high frequency components to the edge of the original image (B) Image segmentation and edge detection High frequency components extracted image (C) image feature extraction Shape features: Fourier descriptors Texture: directly through the Fourier coefficients to calculate the texture features Other features: The characteristic value extracted by Fourier transform to the characteristics of translation, scaling, rotation invariance (D) image compression Fourier coefficients can be directly compressed data commonly used discrete cosine transform is the Fourier transform of the real transformation 朗读显示对应的拉丁字符的拼音)