curves-movement-by-level-set-method

说明：  利用小波进行边缘提取，比较各种小波核函数的能力，体会“数学显微镜”的妙用，例子：车牌定位。(using wavelet edge detection, comparison of different wavelet function of the nuclear capability and experience "math microscope," the magic examples : positioning plates.)

在静态背景下的运动目标检测程序，在windows 7 系统下运行的MATLAB版程序代码(moving object detection)

计算指纹方向场的代码。。。。。。。。。。。。(code for computing orientation of fingerprint )

用于将视频文件转换成帧图像、帧图像转换为mat语言等(Converts a video file to a frame image, then converts it into a number matrix and the inverse process)

本实验要求编写一个计算压缩-解压缩图像的均方根误差、均方信噪比的程序。该程序是一个通用程序。编写程序产生图示的结果。使用上面的保真度准则程序计算可视信息的损失特性。(Prepared in this experiment, a calculation of compression- decompression of the root-mean-square error of image, mean square signal to noise ratio of the procedure. The procedure is a common procedure. Picture shows the preparation of the results of procedures. The use of the above criteria for the fidelity of calculated loss characteristics of visual information.)

说明：  图像融合，实现两幅图像合成一张宽视野的广角图(Image fusion, the two images are combined into a wide field of view)

I WANT SOURCE CODE FOR IMAGE SEGMENTATION

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)

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

将原始载体图片经过一维冗余离散小波变换分成四个子频带，分别对每个子频带进行奇异值分解， 将水印图片灰度值直接按比例缩小嵌入到奇异值矩阵中，再将此矩阵进行一次奇异值分解得到奇异值当做此子带的奇异值， 重建此子带矩阵，而后将进行反冗余离散小波变换得到嵌入载体后的水印图像。(The original image after the one-dimensional vector redundant discrete wavelet transform is divided into four subbands, respectively, of each sub-band for the singular value decomposition, the watermark image gray value scale down directly embedded into the matrix singular value, then this matrix is ​ ​ a Singular value decomposition singular value as the singular values ​ ​ of this sub-band, the reconstruction of this sub-band matrix and after anti-redundant discrete wavelet transform watermark image after embedding the carrier.)