Image-translation-transformation

实现对图像的经验模态分解，得到经验模态分解后的第一层IMF分量，反映了图像的主要信息，去除冗余信息，为识别提供了基础。(Empirical mode decomposition of image, obtain the empirical mode decomposition of the first layer of IMF component, reflected the main information of the image, to remove redundant information, provide the basis for identification. )

2011年比较流行的简单快速的运动目标检测代码。(More popular in 2011, a simple and rapid moving target detection code.)

基于非局部图像去噪模型的遥感图像融合的变分方法。该变分方法采用梯度下降法。(Variational method of remote sensing images nonlocal image denoising model fusion. The variational method using gradient descent.)

图像处理系列之边缘检测篇 涵盖内容： 1>Roberts算子 2>Prewitt算子 3>Sobel算子 4>Laplacian算子(Edge Detection)

说明：  用VB做的图像二值化的代码,希望能对大家有帮助(vb image test)

通过GDAL读取常用遥感影像，如img，tiff，可实现读取显示和雷达影像处理的一些基本算法(Read through GDAL common remote sensing images, such as img, tiff, read the display and radar can achieve some of the basic image processing algorithms)

使用VC++编程，自动检测角点，实现图像配准，代码可用。(Automatically detect the corner points, for image registration, the code is available.)

著名专家写的计算机视觉方面的原著，希望对大家有所帮助(Writing a well-known experts in the computer vision aspects of the original, and they hope to help everyone)

cvi图像设计替换背景 扣图专用代码 毕业设计可用(cvi replace the background of graphic design graduate design buckle Figure specific code available)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )