LdA350

用matlab实现levelset水平集分割图像！(levelset to fen ge Image ag dag gf a ga ga h agr d we v g)

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.)

说明：  这个例子展示了如何使用proj_match_points_transformtion_ransac在拼接应用程序中观察两个图像。(This example shows how to use proj_match_points_distortion_ransac tomatch two images in a mosaicking application.)

基于FCM和水平集的图像分割，以脑颅图像为例，分割结果良好(FCM and the level set based image segmentation to skull image, for example, good segmentation results)

基于Matlab实现的傅立叶梅林变换的源程序代码(Based on Matlab implementation of the Fourier transform of the source code Merlin)

整理了五种多曝光图像融合的代码，用于图像处理，图像去雾，有英文介绍(Five kinds of multi-exposure image fusion codes are sorted out.For image processing, image defogging, with English introduction)

采集一幅像素大于64*64黑白图像； 常规的数学统计数据处理：计算图象各象素点灰度值得均值、标准差、方差，并绘出灰度直方图； 采用[FFT（傅立叶变换）]对图像进行分析(Acquisition of a pixel is greater than 64* 64 black and white images The conventional mathematical statistical data processing: calculation of each pixel grayscale image is worth the mean, standard deviation, variance, and draw grayscale histogram The [FFT (Fourier transform)] to do image analysis )

道路提取，设置初始的点，输入图像，snake 模型(Road extraction, set the initial point, input image, snake model)

基于CFA（颜色滤波阵列）插值检测的图像篡改检测程序(Image tamper detection based on CFA (color filter array) interpolation detecting.)

克里金插值的3D算法，forrtran源代码。 可直接使用。 (Kriging 3D algorithm, forrtran the source code. Can be used directly.)