图像处理中的图像变换的源码与变换后的结果图，包括dct变换，dft变换，w...

matlab写的一个经典的用于计算光流的工具箱。-Matlab write a classic optical flow used in the calculation of the toolbox.

VC读取tiff格式影像文件，很适合初学者参考-VC reads the tiff image file formats, it is suitable for beginners Reference

对高斯图像的参数设定，大图像小图像的参数设定，高斯图像相减，以及图像结果保存-On the Gaussian image parameter settings, large images of small image parameter settings, Gaussian image subtraction, as well as the preservation of image results

一个很不错的blob算法，可以实现对blob图形的track，并显示边缘。-A very good blob algorithm, can be achieved on the blob graphics track, and shows the edge.

这是一个测试图像峰值信噪比的程序源代码，纯C环境下的-This is a test image PSNR the source code, pure environment of the C

计算图像HSV分量的数值，可用于图像颜色分割的统计-Calculated image HSV component values can be used for image color segmentation statistics

程序代码说明 P0401：用Prewitt算子检测图像的边缘 P0402：用不同σ值的LoG算子检测图像的边缘 P0403：用Canny算子检测图像的边缘 P0404：图像的阈值分割 P0405：用水线阈值法分割图像 P0406：对矩阵进行四叉树分解 P0407：将图像分为文字和非文字的两个类别 P0408：形态学梯度检测二值图像的边缘 P0409：形态学实例――从PCB图像中删除所有电流线，仅保留芯片对象-Procedure Code Description P0401: detection using Prewitt operator Edge P0402: different σ value Log Operators image edge detection P0403: using Canny edge detection image edge P0404: Image Thresholding P0405: water line threshold Image segmentation method P0406: for the matrix quadtree decomposition P0407: the image is divided into text and non-text for these two types of P0408: morphological gradient detected binary image of the edge of P0409: morphological examples- from PCB image delete all current lines, retaining only the target chip

用块匹配法进行运动估计的matlab实现方法，返回运动矩阵-block matching method used for motion estimation method of Matlab, the return movement Matrix

图像增强（基于利用模板操作的平滑处理，中值滤波处理）-Image enhancement (based on the use of templates smoothing operations, median filtering treatment)

针对传统方法难以可靠估计图像中纹理单一区域像素点视差的问题,将纹理分析应用于立体匹配中,提 出图像分块整体匹配的方法。首先用LBP/C纹理分析方法对图像纹理进行描述 然后进行基于区域生长的扩张检 测,得到纹理单一图像块 最后对图像块进行整体匹配,得到纹理单一区域的稠密视差图。对国际标准图像进行测试, 结果表明该算法能提高纹理单一区域稠密视差图的精度,具有实用价值 -Due to the difficulty of getting disparity of less textured pixels with traditional approach, block matching algorithm with texture analysis was put forward. First, the images were described by LBP/C texture analysis. Then the expansion detection based on region growing was performed, and the less textured blocks were acquired. Finally the dense disparitymap of less textured area was obtained through block matching. The proposed algorithm was tested with the international standard image data and comparedwith some existing algorithms. The experimental results show that the proposed algorithm can improve the accuracy of disparitywhen handling less textured areas and can be used in practic