image sinusoidal noise filtering. Quite a procedure!

实验目的： 1） 图像水平集合 2） 图像加密算法 3） 边缘提取 4） 噪音图像的产生 （2）显示图像，理解图像的模型 （3）使用图像的不同格式，理解图像压缩的意义 （4）对图像做简单的操作 本次实验需要熟练使用以下几个MATLAB命令： imread, imwrite, imshow,figure -Experimental purposes: 1) the level of collection of image 2) image encryption algorithm 3) Edge 4) noise generated images (2) display images, understanding image model (3) the use of images in different formats, understanding the significance of image compression (4) image to do a simple experiment needs to operate the skilled use of the following MATLAB command: imread, imwrite, imshow, figure

高斯滤波先对图像做卷积运算，进行线性滤波，支持替代方式操作。-Gaussian filter to the image so convolution operation, linear filtering, support for alternative means of operation.

VC环境下开发的图像处理基本操作代码，很方面用，基本包括所以的对图像的操作，对初学者帮助很大。-VC environment to develop the basic operation of the image processing code, it is spent, so basic, including the image of the operation, of great help for beginners.

图像处理中，采用均值滤波的方法对所选取的图像在MATLAB环境中进行处理 -Image processing, using the method of average filtering of the selected image in the MATLAB environment to deal with

把图像处理的MATALAB程序转化为C++程序-put image processing procedures into MATLAB C Program

图像分割加特征提取的算法，一起分享，共同研究，共同进步-Add feature extraction image segmentation algorithms, along with sharing, joint research, and common progress

实例5 页码：34 稿件名称：VC图像处理－－快速傅立叶变换篇 稿件作者：耿迅 程序名称：ImageTest 运行环境：Win+VC6.0 注意事项： 由于本程序使用了VFW组件，所以在Project－－Setting中－－Object/libray modules中添加vfw32.lib。 -err

手写数字识别，能识别0-9，识别精度高，程序简单-Handwritten numeral recognition, to identify 0-9, identification of high accuracy, simple

关于XP按钮的一个例子程序，可实现图形一些简单的算法应用-Button on the XP one example of these procedures is to realize some simple algorithm graphics applications

本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!