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用opencv类库两副图像的特征点匹配
- 两副图像的特征点匹配，其中用了opencv类库，功能包括：特征点的亚像素精度检测，特征点初始匹配，基本矩阵计算及对极约束引导匹配等。matcher.rar网上的都是有错误，而且缺少类库，我把错误改正了，把所有需要的类库都加上了，现在已经编译通过，保证能运行！希望大家喜欢,2 points, matching the characteristics of images, which used the opencv library, features include: Feature Point D
raw
- 读取和写入raw格式图像，此格式图像文件存储一幅图中的全部像素点，并以每个像素点16bit数据保存-read and write a raw format 16-bit image
xs
- 将一幅图像的所有的坐标点的像素值显示出来-The coordinates of an image point of all the pixel values are displayed
Colorimagecolorcomponentinter-exchange
- 全彩色图像的色彩分量间互换。对于彩色图像，图像的颜色是由每个像素点的RGB三分量的组合决定的。本实验的实验目的就是通过调整图像像素点RGB三分量的顺序来进行图像红、绿、蓝三个颜色分量间的互相转换，从而改变图像的颜色；比如：原图像为一朵红花，经过处理之后，我们将其变成绿花或者蓝花。运用到canny算子-Full-color images between color-component exchange. For color images, color images are from each pi
ImageContourTtackingAlgorithm
- 图像轮廓跟踪算法： 通过若干个像素点来模拟图像，找出图像的轮廓。 前几页是对算法的描述，最后一页是算法实现的代码-Image contour tracking algorithm
Logistic
- 用logisitic映射生成混沌密码流，对密码流排序后生成置换地址集合，用这个地址集合对图像像素点进行重新排列，从而达到置乱的目的。 -Chaotic maps generated by logisitic password stream flow sorting of passwords generated after the replacement address of the collection, use this address a collection of image pixe
meshpic
- 输入一幅图像，输出图像的像素点的分布的图形显示。-Enter an image,then the output image will display the graphical display of the image pixel distribution .
IMAGEPREGRESS
- 数字图像处理初学者编写的小程序，简单易懂，方便实用；其中包括RGB变GRAY，图像形态学变换（灰度拉伸，窗口变换等），手动去除噪声点，图像边缘提取，图像像素灰度值输入TXT文档等。-something about image pregress.
MyImageProcess
- 对BMP格式图像像素点进行任意的读写显示，并且可以进行效果变换-BMP format images on the pixel display any of the read and write, and can effect change
image_fft2_and_ifft2
- 本程序是基于VC6.0平台的图像傅里叶变换及反变换，可支持读入、显示各种类型的彩色、灰度图像，并且可以方便地获取图像中任意像素点的灰度值。图像的傅里叶变换及反变换通过菜单方式执行，且代码清晰，执行效率高。-This procedure is based on VC6.0 platform image Fourier Transform and inverse transform, to support reading, showing all kinds of color, grayscale
imageHarris
- Harris 角点检测计算的依据是图像像素点的梯度,并且受约于像素之间的相关性. 而图像质量直接影响像素 之间的相关性,从而对Harris 角点检测产生作用. 本文根据图像质量的几个标准分别对图像进行评价,并阐述图像 质量和Harris 角点检测之间的关系,把图像质量作为Harris 角点检测选择参数的一个依据,并建议通过量表对不 同质量的图像选择不同的参数进行角点检测.-Harris corner detection is based on the pixel gradient o
chap1_4
- C++环境下基于单文档的图像的读入，并在此基础上访问图像像素点，代码中有详细的中文注释，对于初学者很有帮助-C++ environment based on a single document, read in the image, and on this basis, access to image pixels, the code in detail in the Chinese comments, very helpful for beginners
5初级图像混合
- 编程环境：VS+OPENCV ，对于初学者学习基于opencv的图像处理非常有帮助，此节点主要是对像素点的一些处理。(Programming environment: VS + OPENCV, for beginners to learn based on opencv image processing is very helpful, this node is mainly on the pixel some of the processing.)
4遍历图像像素的14种方法
- 编程环境：VS+OPENCV ，对于初学者学习基于opencv的图像处理非常有帮助，此节点主要是遍历像素点的一些方法。(Programming environment: VS + OPENCV, for beginners to learn based on opencv image processing is very helpful, this node is mainly traversing the pixel some of the methods.)
3用动态地址计算配合at访问像素
- 编程环境：VS+OPENCV ，对于初学者学习基于opencv的图像处理非常有帮助，此节点主要是用at访问像素点的一些方法。(Programming environment: VS + OPENCV, for beginners to learn based on opencv image processing is very helpful, this node is mainly used to access some of the pixels point method.)
2用迭代器访问像素
- 编程环境：VS+OPENCV ，对于初学者学习基于opencv的图像处理非常有帮助，此节点主要是用迭代器访问像素点的一些方法。(Programming environment: VS + OPENCV, for beginners to learn based on opencv image processing is very helpful, this node is mainly used to access the pixel iterator some of the methods.)
1用指针访问像素
- 编程环境：VS+OPENCV ，对于初学者学习基于opencv的图像处理非常有帮助，此节点主要是用指针访问像素点的一些方法。(Programming environment: VS + OPENCV, for beginners to learn based on opencv image processing is very helpful, this node is mainly used to access some of the pixels of the pointer method.
点运算
- 数字图像处理中的各种点运算，用于解决图像局部像素点的变换，从而解决图像优化问题。(Digital image processing in a variety of point operations, used to solve the image of the local pixel transformation, thus solving the image optimization problem.)
Tuxiang
- 各种图像像素点的处理，包括线性化处理，模板操作等等(Processing of pixel points of various images, including linearization, template operation, and so on)
109201252image-processing
- 关于MATLAB图像相关性分析的问题。随机选取1000对像素点，进行水平和垂直相关性分析。(image-correlation. select 1000 pair of pixel randomly and analyze the correlation both horizontally and vertically)