这是本人毕设时所作的医学图像处理源代码，供初学者参考

利用小波变换技术图像融合方法从模糊图像中恢复图像，并给出结果-Using Wavelet Transform Image Fusion technology of fuzzy images from the resumption of images, and gives results

dicom 的c#库，包含各种dicom 对象，vr,dataset等-dicom of the c#, dicom contains various objects, vr, dataset, etc.

此源码,对图像的自动阈值,与局部阈值是很有效果,希望对大家有所帮助-This source of images automatically threshold, and the local threshold is very effect, and they hope to help everyone

国外姿势识别的经典源码，好东西大家一定要珍惜-Gesture recognition abroad classic source, good things everyone must cherish

采用hough变换检测圆的仿真程序，希望对大家有用。-Hough transform circle detection using the simulation program, in the hope that everyone useful.

基于轮廓的形状特征提取 1)该程序在VC＋＋6.0下编译通过； 2)测试图片说明：（图像路径名请在main函数中fp=fopen("1.bmp","rb")中设定） 64×64 24bit BMP： 图像1、2用来验证旋转图像识别；图像1、3用来验证平移图像识别；图像1、4用来验证缩放图像识别； 120×120 24bit BMP： 图像5、6用来验证平移图像识别；图像7、8用来验证旋转图像识别； 3)结果说明： MCS.dat保存了Mountain Climbing Sequence。 -Based on the outline of the shape feature extraction 1) the program in VC 6.0 under the compiler through 2) Test Photo Caption: (image path name in the main function fp = fopen (1.bmp, rb) set) 64 × 64 24bit BMP: image 1,2 to verify rotating image recognition image 1,3 to verify translational image recognition image 1,4 to verify scaling image recognition 120 × 120 24bit BMP: images used to verify translational 5,6 image recognition images 7,8 rotate images used to verify identification 3) The results indicate that: MCS.dat preserved Mountain Climbing Sequence.

一个经典的数字图像处理程序精华集，提供了数字图像的一些最基本的处理-a classic digital image processing procedures set essence, providing a digital image processing some of the most basic

BMP与JPG相互转换的源代码，VC++6.0-BMP and JPG conversion of source code, VC++ 6.0

关于图像模式识别的代码和算法的重要研究，很不错-Image data source is very improtant

：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.