图像缩放，更好

用C语言开发的图像的维纳滤波处理，算法经典-C language development with the image of the Wiener filter processing, the algorithm Classic

在计算机视觉中首先一步是计算每个摄像机的参数(外参和内参)，这是重建物体所必须的，这一个过程称为摄像机定标(camera calibration)。这是我自己写的一个简单的采用2维模板(模板是由黑白相间的格子组成，格子大小60cm×60cm)定标的程序，交互性比较好，每个角点通过鼠标点击自动获取。-Computer Vision in the first step is to calculate the parameters of each camera (outside the Senate and internal), which is necessary for the reconstruction of objects, which a process known as camera calibration (camera calibration). This is what I wrote it myself using a simple 2-D template (template is composed of black-and-white lattice, lattice size of 60cm × 60cm) calibration procedures, better interactivity, each corner through the automatic acquisition of mouse clicks.

快速傅里叶变换程序，用MFC详细编程

Active contour demo implements snake, which essentially is an A* search. This can be used for image registration, segmentation, feature extraction.

一些关于计算机图形学的flash文件，可以更好的学习图形学的编程思想，讲述了各种划线的方法-some flash file about computer figure programming

能够对bitmap图片进行无级缩放的动态库源代码-able to bitmap picture without the dynamic zoom level for the source code

本程序采用综合多种图象处理方法对输入彩色图象进行增强处理,处理速度快. -this programme is used some kind of methods to enhance the imput color image

CSutherland是图形学中著名的图线分割算法之一，本程序提供了该算法的实现，有兴趣的可以下载学习一下-CSutherland graphics is well-known drawings segmentation algorithm, The procedure of the algorithm, and it is interested in learning what can be downloaded

采用多孔trous算法(undecimated wavelet transform)实现小波变换，用的是一维信号 -The use of porous trous algorithm (undecimated wavelet transform) the realization of wavelet transform, using a one-dimensional signal

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。-simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.