图像的快速傅立叶变换，基于VC++开发平台。

傅立叶描述子，用于提取形状的特征。c++编写-Fourier descriptors for the extraction of shape features. c++ prepared

opencv基础配套光盘代码，与大家分享-opencv supporting CD-ROM based on code sharing with U.S.

Using Data Acquisition Toolbox to process data from hardware

高斯随机噪音程序，做理论模型数据时，一般会加高斯随机噪音，以模拟实际数据-Gaussian random noise process, make the theoretical model data, they generally will be Gaussian random noise to simulate the actual data

边缘检测与分割源代码 edgedetect.m, houghtr.m, quadtree.m, regrow.m, sobeledge.m, straightline.m, thresh.m, watersd.m 算法包括Prewitt、Roberts、Log、Canny、Sobel算子实现边缘检测，基于Hough变换的直线检测，4叉树区域分割，分水岭分割。（算法对应源文件名）-Edge Detection and Segmentation of the source code edgedetect.m, houghtr.m, quadtree.m, regrow.m, sobeledge.m, straightline.m, thresh.m, watersd.m algorithm includes Prewitt, Roberts, Log, Canny, Sobel Operator realize edge detection, Hough Transform-based line detection, 4-tree region segmentation, watershed segmentation. (Algorithm for the corresponding source file name)

彩色图像分割算法，首先基于直方图粗分，而后通过kmeans细分，得到分割结果-Color image segmentation algorithm, first, rough segmentation based on histogram and then broken down by kmeans get segmentation results

第二届全国高校 BBS 程序开发大赛算法组2-Second National College BBS Development Competition Algorithm Group 2

图像修复。它可以自动恢复图像。

LensPhoto，世界领先的数字近景摄影测量系统软件2007版，绝对值得收藏。-LensPhoto, the world" s leading digital close-range photogrammetry system software, 2007 edition, is absolutely worthy of collection.

本文提出了一种新的跨国家的障碍 检测技术为基础的立体视觉系统。 原始图像的预处理的高斯 过滤器和对比度限制的自适应直方图 均衡（ CLAHE ）方法来削弱作用 噪音，光线和对比度。哈里斯的角落位于与子像素精确。 -Cross-country intelligent vehicles always work in complicated environments with varying illuminations. The paper presents a new cross-country obstacle detection technology based on stereo vision system. The original images are preprocessed by Gaussian filter and contrast-limited adaptive histogram equalization (CLAHE) method to weaken the effect of noise, light and contrast. Harris corners are located with sub-pixel accurate. To guarantee the overall system real-time performance, feature-based matching techniques are studied and fundamental matrix is calculated based on random sample consensus (RANSAC). Also restrains are studied to eliminate pseudo matching pairs. Then data interpolation is introduced to build elevation maps. Edge extraction and morphological processing are concerned to accomplish obstacle detection. Experiment result