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基于区域的稠密立体匹配方法（论文） 关键词：种子点；区域增长；稠密匹配(Based on Region Dense Three—dimensional Match Method)

模式识别经典方法之一 K均值算法（KMeans）进行图像分割 有完善的图形界面 可以通过柱状图的形式动态展示聚类程序(One of the classical method of pattern recognition K means algorithm (KMeans) for image segmentation has improved graphical interface can be dynamically displayed in the form of histogram clustering procedure)

四叉树分割 opencv 编写 读入灰度图 进行递归分割(Quadtree segmentation opencv read into grayscale prepared for recursive partitioning)

说明：  对给定图片求图片中的染色体个数。具体步骤：中值滤波方法，二值化处理，膨胀和腐蚀，开闭运算，求连通区的个数(Of a given picture for the picture of the chromosome number. Concrete steps: median filter method, binarization processing, expansion and corrosion, opening and closing operations, and the number of connected areas)

本程序基于近景摄影测量，单片空间后方交会算法完成，用C++代码编写，内含有样本数据，以供试验。运行正常，注释完整。(This procedure based on the close shot photogrammetry, monolithic space resection algorithm, contains sample data, and written in c++ code for test. Running normally, notes complete.)

重要的纹理分割分类基础程序，可以进行更深入的探讨和优化，应用于遥感影像中。(Important basis for classification of texture segmentation procedure can be carried out more in-depth exploration and optimization, application of remote sensing images.)

LSB替换隐写软件，该软件能把ASCII码嵌入到图像中，其载体图像是24位的BMP格式图像(LSB Steganography tool which provides hide ASCII infomation within 24-bit Bitmap images)

基于vc++6.0的SURF算法的程序代码，可以直接运行(Based on the SURF algorithm for vc++6.0 program code can be run directly)

这是一个比较全面的傅里叶变换，例子中含有测试的图片，能让人更加明白傅里叶变换的处理效果。(This is a more comprehensive Fourier transform, the example contains a picture of the test, can make people more aware of the Fourier transform of the treatment effect.)

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.