vc++和OpenCV结合找出图片中的角点，并且做出标记。此程序同时可以用于图片特征点的提取，用于特征匹配的后续开发做准备。...

mosaic given image using training images

awimg=rgb2gray(im1) tic [accum, circen, cirrad] = CircularHough_Grd(rawimg, [20 30],5,50) circen toc figure(1) imagesc(accum) axis image title( Accumulation Array from Circular Hough Transform ) figure(2) imagesc(rawimg) colormap( gray ) axis image hold on plot(circen(:,1), circen(:,2), r+ ) for k = 1 : size(circen, 1), DrawCircle(circen(k,1), circen(k,2), cirrad(k), 32, b- ) end hold off title([ Raw Image with Circles Detected , ... (center positions and radii marked) ]) figure(3) surf(accum, EdgeColor , none ) a

张兆理编著现代图像处理技术及Matlab实现-Zhao-Li ed of modern image processing techniques and Matlab implementation

图像处理作业，能够处理BMP格式的图像，能够实现多达29种效果。是图像处理方面的一个很好的演示程序-image processing operations, will be able to handle BMP format images can be achieved up to 29 types of effects. Image processing is a good demonstration program

基于OpenCV和forbcb的人脸跟踪程序

本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!

这是一个非常好的图像二值化电路的源代码，希望能帮助大家-This is a very good image binarization circuit

轮廓提取,本设计通过边缘算法,达到高效低损失的计算效果,有效的消除轮廓边缘的噪音影响.-Contour extraction, through the edge of the design algorithm, to achieve high efficiency and low loss calculation results, effectively eliminate the impact of the noise contour edge.

对MR图像进行各项异性滤波。能在同质区域平滑噪声，边界处不被平滑。-process the MR image using anisotropic filter. Smooth noise in homogeneous regions, the boundary is not smooth.

本MATLAB程序 用MATLAB 来实现3D 图像的匹配 用高斯卷积 来寻找 关键点 ,再利用关键点处的特征向量 通过3D向量的匹配 来实现3D 图像的配准-The MATLAB program using MATLAB to realize the matching of 3D images using Gaussian convolution to find the key points, re-use the key points of the feature vector through the 3D vector matching to realize 3D image registration