小波模极大值程序

实现了Jakob Wilm & Hans Martin提出的ICP算法，配准两片点云。(The ICP algorithm proposed by Jakob Wilm & Hans Martin is used to register two point clouds.)

用小波变换作为稀疏基，采取OMP算法将图像重建恢复，由于算法计算量大会导致成像时间过长，程序用改进的分块处理缩短了时间，(Wavelet transform as a sparse base, take OMP algorithms to restore the image reconstruction algorithm to calculate the General Assembly led to the long imaging time, the program using a modified block processing to shorten the time)

对图像进行分块处理，可以设置分块比例，该程序设置的是8*8的比例，将图像分成64块(The image processing block, you can set the ratio of the block, the program is set proportion of 8* 8, the image is divided into 64)

1.运用Laplacian算子，基于二阶微分的图像增强，即拉氏变换法进行图像平滑 2.利用邻域平均法实现图像的平滑处理 3.利用Robert算子做锐化处理(1. The use of Laplacian operator, based on second-order differential of the image enhancement, that is, Laplace transform method for image smoothing 2. The use of neighborhood average method of smoothing image 3. The use of Robert sharpening operator to do)

通过dct的8*8块的变换量化操作结合水印生成水印图像，其中包含几种常见的攻击测试。(The program generates watermark image through DCT transform.)

对图像添加各种噪声 高斯噪声 淑盐噪声(Add a variety of image noise, Gaussian noise, noise, salt-sook)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

检测前跟踪，使用phd方法，phd实现是检测前跟踪是最新技术，可以提高跟踪性能(Detected before tracking method using phd, phd implementation is detected before tracking is the latest technology that can improve the tracking performance)

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.)

可以将连续图片转换成视频文件，值得参考学习(Convert a continuous image into a video file)