使用vc编写的一个光流原程序 使用了hs算法 没有解压密码...

采用多孔trous算法(undecimated wavelet transform)实现小波变换,用的是一维信号-The use of porous trous algorithm (undecimated wavelet transform) the realization of wavelet transform, using a one-dimensional signal

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.

算法功能：夜间灰度图像的增强处理 参考论文：《一种用于夜间图像增强的算法》，实现的效果要好于直方图均衡化的效果。-Algorithm functions: gray-scale images to enhance night-time deal with the reference paper:

图像处理－形态学处理－图像粗化和细化源程序-Image Processing- morphological processing- image of rough and refined source

基于matlab的小波去噪研究，有参考价值-Based on the wavelet denoising matlab study has reference value

基于kalman滤波器的2D目标跟踪，maltab实现-2D-based kalman filter target tracking

包含了很多实用的小程序，很适合初学者熟悉认识matlab，从而进一步加强认识-Contains many useful little program, it is suitable for beginners familiar with the awareness of matlab, so as to further enhance understanding of

采用灰度变换的方法增强图像的对比度 P0304：直方图均匀化，采用MATLAB编译-Gray-scale transformation using the method of contrast-enhanced images P0304: Histogram of homogenization, using MATLAB Compiler

简介： 一个能产生多种火焰效果的控件示例及源码。研究用非常适合的一个实例！-description : a flame can produce a variety of effects and the control sample source code. Research is a suitable example!

：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.