本程序是VC++数字图像识别技术典型案例一书的所有程序，很有参考价值...

西安交通大学开的《图象处理》的课，用的课本是章毓晋老师编的《图象工程》上册，既《图象处理和分析》，我本科期间学的这门课，这是用MATLAB所做的作业源程序，包括颜色模型转换成，直方图转换，图象分割，边缘检测，hough变换（哈夫变换），图象表达等。-Xi"an Jiaotong University opened the "image processing" of the class, Textbooks are used in Yu-Jin Zhang teachers are "image projects" on the list, both "image processing and analysis" During my undergraduate study of this course, this is done using MATLAB source operations, including the color model conversion. Histogram conversion, image segmentation, edge detection, hough transform (Hough transform), image expression.

很好的聚类代码，可以用于对图像数据聚类。-A good clustering code that can be used to image data clustering.

源码包含了常见的图像形态学算法，如图像基本逻辑运算，阈值分割，边缘处理，孔洞填充等-Source image contains a common morphological algorithms, such as images of basic logic operations, threshold segmentation, edge treatment, holes filled, etc.

图像的纹理分析方法2。―― ―― -image texture analysis methods 2.

将图像从视讯显示的一个区域复制到另一个区域-Images from the video display copy of a region to another region

matlab用hough变换检测圆的程序，经过检测可以运行良好-matlab using hough transform circle detection procedure, after testing can run a good

转动箱子的动画,多维旋转图象,可以多角度观察,利用SIN函数-rotating box animation, multi-dimensional rotating images, more angle, the use of SIN function

VTK与ITK结合读取BMP图片并显示，需要用CMake配置一下，适合有初学者。-Itk combine VTK and read BMP picture and show that you need to use CMake to configure, suitable for beginners.

偶然找到一个VC源码，用它里面的类可以产生类似于JAVA应用中的水纹、火焰以及熔浆效果。本人感觉这个代码非常棒(很COOL)，不敢独享，特拿出来和大家(同道中人)一起分享!-occasionally find a VC source, using it inside the class can have a Java application similar to the watermarked, flame and melts effect. I feel pretty good this code (very COOL), not exclusive, special show to you (fellow human) to share!

：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出 应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音 乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高 斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪 方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the traditional de-noising method in case of strong background noise, the ability to extract the voice signal even weaker failure, the application of independent component analysis (ICA) method of voice signal feature extraction, and prove that this transformation can be enhanced voice ICA and music of super-Gaussian signals. On this basis, the application of ICA basis function as a filter, through the threshold of the denoising method of Gaussian background noise contains strong voice signal denoising simulation. The results show that this method is obviously superior to the traditional mean filtering and wavelet denoising methods for the strong background noise under the weak signal detection provides a new way.