ruanyuzhiquzao

这是一个关于matlab图形变化的程序，其主要有三个图形，并且能改变其颜色的程序(This program is a a matlab graphical changes, its three graphics, and can change its color program)

说明：  一种人脸识别方法,是利用眼睛虹膜进行识别(A face recognition method is the use of iris identification)

说明：  压缩感知的经典代码，包括观测，稀疏表示，重建，其中观测用的高斯观测矩阵，稀疏用的DCT，重建用的OMP算法(Compressed sensing of the classic code, including observations, sparse representation, reconstruction, observed with a Gaussian observation matrix, sparse use of the DCT, re-use of the OMP algorithm)

利用相位相关法对图像进行配准，配准精度可达到0.01(The registration accuracy can reach 0.01 by using phase correlation method.)

curvelet图像融合，一个很好的程序，对大家应该有帮助(curvelet fusion)

说明：  使用LazySnapping算法对GrabCut算法进行改进(There are advanced segmentation algorithms in the literature which extends the concepts of graph cuts. Most prominent of them are Graph cuts and Lazy Snapping. Lazy napping is an interactive image cut out tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy.)

基于小波变换的图像分割算法，它实现了论文 Wavelet Based Bimodal Image Segmentation for Blob Exctraction... 的算法。(This code is a part of work Wavelet Based Bimodal Image Segmentation for Blob Exctraction... .)

sar图像的基于R-D模型的几何纠正，主要是利用RD模型的数学公式进行相关的几何纠正(Geometric rectification based on RD model of SAR)

说明：  數據繪圖時候有時候必須處理資料矩陣轉向，該編碼詳細說明如何處理矩陣在三維度空間下進行轉向，而不是利用Curve Fitting Tools(Data drawing sometimes has to deal with the data matrix steering, which details how to handle the matrix to steer in a three-dimensional space instead of using Curve Fitting Tools)

编程实现二维8*8图像块的DFT，DCT变换 输入一副RGB图像，将其转化为YCbCr颜色空间，然后对Y分量做分块DFT变换，保存为灰度图像，统计计算时间 编程实现8*8块DCT变换,然后分别显示，当使用64个DCT系数里面的一些系数进行重构，比较其质量；同时熟悉采用FFT来实现DCT变换的思想，并显示2维8*8DCT变换的基函数图像(Programming the DFT, DCT transform of two dimensional 8*8 image block Input a pair of RGB images, transform it into YCbCr color space, then divide the Y component into block DFT transform, save as gray image, and calculate the calculation time The 8*8 block DCT transform is implemented by programming, and then it is shown that when using some coefficients in 64 DCT coefficients, the quality is reconstructed. Meanwhile, the idea of DCT transformation is familiar with FFT, and the base function image of the 2 dimensional 8*8DCT transform is displayed.)