Using wavelet transform methods of image enhancement as a treatment procedure

用拉普拉斯算子对图像进行锐化处理，从而可以提起图像边界，附源码和测试图片-With Laplacian sharpening image processing, leading to the institution of the image border, with source code and test images

用于提取图像一些的基本特征，方便进行图像分类-Used to extract some basic characteristics of images to facilitate image classification

不用MATLAB自带函数求解灰度图像的直方图-MATLAB function to solve do not own histogram

图像处理源代码，Hough变换检测椭圆。-Image processing source code, Hough transform ellipse detection.

：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.

指纹识别 图像处理 细化 平滑 处理算法-Fingerprint image processing refined smoothing algorithm

VC++实现的物体跟踪程序 opencv 效率还可以，挺不错的-object tracking

matlab写的一个经典的用于计算光流的工具箱。-Matlab write a classic optical flow used in the calculation of the toolbox.

非局部去噪算法-nonlocal denoise 源码-Non-Local Denoising Algorithm-nonlocal denoise source

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!