Gaussian_plume

说明：  这个程序用于在图片中增加各种噪声，如高斯椒盐噪声, 加性或乘性等多种混合噪声，用于其它程序的测试。(This procedure is used to increase the variety of picture noise, such as salt and pepper Gaussian noise, additive or multiplicative noise, such as a mixture for testing other programs.)

说明：  shepp-logan的2D直接傅里叶反变换法(Shepp-logan 2-D Direct Fourier Inverse Transform Method)

本例代码需要专门的vrml浏览器浏览，本例代码是一般酒店，公司等场所的旋转门，在建大型三维场景时可以直接链接此例插入场景(This case, the code requires specialized vrml browser browsing this case, the code is the revolving door of the place of the general hotel companies in the construction of large three-dimensional scene can link directly to this case, insert scene)

3D text editor to show text in 3D visualized

计算机视觉方面非常好的一本专业书籍，英文版PDF。详细介绍了计算机视觉领域运用的射影几何射影变换理论知识，介绍了相机标定的理论和方法，还有三维重建，立体视觉等方面的基础知识，一本不可多得教材！其中书中的代码实现都可以在网上下载，推荐！(Computer vision is a very good professional books, in English PDF. Details of the use of projective geometry projective transformation of theoretical knowledge in the field of computer vision, introduces the basic knowledge of the camera calibration theory and methods, as well as three-dimensional reconstruction, three-dimensional vision, a rare textbook! The implementation of the code in the book can be downloaded from the internet, recommended!)

这是一个非常好的图象分割程序,基于纹理分割的,保证别人没有上传过(very good image segtation)

meanshift图像跟踪算法，实现目标物体的连续跟踪(meanshift image tracking algorithm to achieve continuous tracking of the target object)

说明：  结构光三维重建，用来生成点云，精度较高，速度较慢。(Three-dimensional reconstruction of structured light)

说明：  图片结构相似度评价，附加MSE,SNR,PSNR等作为对比参数(Photo structure similarity evaluation, additional MSE, SNR, PSNR, etc., as comparative parameters)

一个比例和方向自适应均值漂移跟踪算法（SOAMST） 提出本文所要解决的问题，如何估计的规模和方向 改变均值漂移下的目标跟踪框架。在原来的均值偏移 跟踪算法，可以很好地估计目标的位置，规模的同时， 方向的变化，不能自适应估计。考虑到图像（重量） 是来自于目标运动模型和候选模型可以代表的可能性，一个 像素属于目标，我们证明了原来的均值漂移跟踪算法可以 推导出的重量图像的零阶和一阶矩。随着零阶 矩和目标模型和候选模型之间的Bhattacharyya系数， 提出了简单而有效的方法来估计的规模为目标。然后一种方法， 利用估计的区域和第二阶中心矩，提出 自适应地估计目标的宽度，高度和方向的变化。广泛 实验来证实所提出的方法，并验证其可靠性 规模和方向变化的目标。(A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, we show that the original mean shift tracking algorithm can be derived using the zeroth and the first order moments of the weight image. With the zeroth order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilizes the estimated area and the second order center moment, is proposed to adaptively e)