GrayGradinetGSJZ

基于彩色信息的道路图象分割,可以有效的去除道路上的阴影.(Based on color information of the road image segmentation, can effectively remove the shadow on the road.)

实现shift算法，进行精确的图像识别，可以抵抗各种旋转、平移、缩放等仿射变换的攻击(Realize shift algorithm, an accurate image recognition, you can resist all kinds of rotation, translation, scaling affine transformation attack)

图像简单处理小程序，带有测试图片，比较实用的小冬冬 (Simple procedures to deal with small images, with the test pictures, more practical small Dongdong)

基于粒子滤波和卡尔曼滤波的运动目标视频跟踪程序，非常强大和实用，强烈推荐学习使用。(Kalman filter based on particle filter and target video motion tracking program, very powerful and practical, it is strongly recommended to learn to use.)

对比了常见的几种去噪方法，含中值滤波，均值滤波，维纳滤波，高斯滤波，以及三种形态学滤波（一般的，改进的，多结构元素形态学滤波）(Comparison of several common Denoising containing median filtering, mean filtering, Wiener filtering, Gaussian filtering, as well as three morphological filtering (generally improved, multi-structural elements morphological filtering))

说明：  根据：一种可识别破碎图形的特殊广义Hough变换方法 论文，在matlab上做的实验源码，有模版生成，广义哈夫变换代码(In accordance with: A broken identifiable graphics special generalized Hough transform methods papers, in matlab to do experiments on the source, there are template generation, generalized Hough transform code)

实现图像下采样，实现的采样方法有最邻近采样法、二次插值法、双三次卷积法。(Image sampling, the sampling method to achieve the nearest neighbor sampling method, the quadratic interpolation, bicubic convolution.)

说明：  LBP算法实现图像的纹理分类，基于论文：multiresolution gray scale and rotation invariant texture classification with location binary patterns(LBP algorithm to achieve image texture classification, based on the paper: Multiresolution gray scale and rotation variant texture classification with location binary patterns)

This program draws an image from a bmp file

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )