voxel_src

实现如何使用顶点vertex来创造一个正方体(Implement a cube by using vertex)

在matlab开发环境中，基于8近临的贴标签算法，实现不同连通域的区分(In the Matlab development environment, based on eight nearly labeling algorithm to achieve the distinction of different Unicom domain)

压缩传感重构算法：稀疏基采用离散余弦变换的omp重构算法(Compressed sensing reconstruction algorithm: sparse basis using the discrete cosine transform reconstruction algorithm omp)

二维磁流体计算程序，可根据问题干煸，已有改变成三维的版本(Two-dimensional MHD calculation program, according to the problem dry stir, has changed into a three-dimensional version of the)

斯坦福大学的点云数据，龙模型，对于学习点云来说是非常好的实验资料(The point cloud data of Stanford University, dragon model, for learning point cloud is very good experimental data)

说明：  对偶四元数应用于空间坐标变换，和对偶四元数和齐次矩阵的转换(Dual quaternion coordinate transformation applied to space, and dual quaternion and homogeneous matrix conversion)

3D8光立方，动画程序！可供下载，几十种动画！(3D8 cube, animation program!Available for download, dozens of animation!)

这是一款做的一个很不错的模型和骨骼动画系统，其中包含了针对3DS MAX的导出插件的源码，可供朋友们参考。(This is a very good models and a skeletal animation system, which contains the exporter plug-in for 3DS MAX source code, for friends as a reference.)

说明：  一种二值水印嵌入的方法，个人编写的，里面还有ppt，及Arnold置乱算法(a watermark embedded value method, personal preparation, there is also ppt, and Arnold Scrambling Algorithm)

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)