electronic-image-stabilization

说明：  MATLAB如何将图片切分成任意M*N块 在图像处理的时候，有时会不可避免的将图像分块来进行操作，但是图像的像素不可能每次都是可以被整除的，所以我们需要imresize函数对图像的尺寸进行修改，以便可以完成M*N块的分割(How can matlab cut the image into any m * n blocks? Sometimes it is inevitable to divide the image into blocks for operation. However, the pixels of the image can not be divided every time. So we need to modify the size of the image with the function of reduce to complete the m * n block segmentation)

说明：  pcnn的matlab实现，pcnn神经网络主要用于图像去噪分割等(the realization of the matlab PCNN, pcnn neural network used mainly for segmentation, such as image denoising)

由rgb转化到ycrcb空间，再又ycrcb空间转化到rgb空间中(make a color space transmission from rgb into ycbcr，then transmit color space from ycrcb into rgb)

labview基础视觉识别的东西，讲的很细(The basic visual recognition course of LabVIEW is very detailed.)

基于ROF全变差分模型，用于图像去噪，并利用split bregman算法进行加速运算(ROF points based on total variation model for image denoising, and using split bregman accelerated computing algorithms)

传统的几种图像增强与去噪方法，包括直方图处理，灰度变换，滤波去噪等几类。细分为直方图均衡化，直方图规定化，灰度线性变换，中值滤波，维纳滤波，同态滤波(The traditional methods of image enhancement and denoising include histogram processing, grayscale transformation, filtering and denoising. Subdivided into histogram equalization, histogram specification, gray level linear transformation, median filtering, Wiener filtering, homomorphic filtering)

一种求解电大平台天线互耦的时域积分方程快速算法(Solving the Antenna TV platform stie fast algorithm)

双数复小波变换及其图像去噪算法。。。。。(Double the number of complex wavelet transform and image denoising algorithm. . . .)

说明：  一个声纹识别的全代码,包括语音信号的预处理,建模,和识别(Voiceprint identification of a full code, including the speech signal pre-processing, modeling, and identification)

本文首先概述了独立成分分析与盲信号分离， 主成分分析的关系， 然后系统地介绍了该技术的基本概念和数据模型， 对现今比较流行的各种独立成分分析算法进行了剖析和总结。最后对独立成分分析的发展及应用前景作了展望(In this paper, an overview of independent component analysis and blind signal separation, principal component analysis of the relationship, and then systematically introduces the basic concept of the technology and data models for today' s more popular variety of independent component analysis algorithm is analyzed and summarized. Finally, independent component analysis of the development and application prospects are described)