Lbp

基于最小二乘法的相位解缠VC++算法 (least squares method based on the VC Phase Unwrapping Algorithms)

计算进口边界条件为超音和亚音条件下，气流流经凸包时的二维流动状况(program for calculate air flows through the convex hullto in the import boundary conditions of supernic and subsonic)

说明：  matlab实现帧间差分提取运动目标，包含测试视频和运行效果。在背景固定的视频中效果较好。附赠lena图片以供CV学习。(Matlab achieves inter-frame differential extraction of moving objects, including testing video and running effects. It works well in video with fixed background. Enclosed Lena pictures for CV learning.)

智能图像处理提出了一种改进的基于特征和回归分析的人群密度估计算法。引入图像中的角点个数作为低层特征(Interactive Images: Cuboid Proxies for Smart Image Manipulation)

该源程序可以实现BMP图片的压缩(BMP pictures)

基于基元共生矩阵的纹理特征提取。通过5个基本的方格来检测图像并生成图像的基元共生矩阵，并从矩阵中提取其纹理特征信息。(Texton occurrence matrix based texture feature extraction)

纯MATLAB代码，不需要在使用SIFTWIN32可以直接运行的代码(100 code of MATLAB,u neednot SIFTWIN32 desinge the map)

该代码实现序列图像配准和重建过程，包含多种算法，配准算法有keren,marcal等，重建算法包含了POCS凸集投影算法(The code sequence of image registration and renewal process, including a variety of algorithms, registration algorithms are keren, marcal etc. reconstruction algorithm contains POCS POCS algorithm)

对视频中的几帧进行移动目标检测，着重标记出移动目标。(Moving targets are detected for several frames in the video, with emphasis on the moving target.)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )