mianhuizhi

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：  应用于非线性电路中的混沌现象的MATLAB程序(Applied to non-linear circuit chaos in MATLAB programs)

说明：  在VC下利用directX9.0写的很好的3D图形编程的例子，包括.x文件的读取，纹理的加载！适合初学者练手！(in VC directX9.0 write using a good 3D graphics programming examples, including. X document read, texture loading! For beginners to practice hands!)

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.)

关于Joseph forward前向投影的系统矩阵的ART三维重建程序，对于学习三维重建的同学有很大的帮助！(About ART reconstruction program before Joseph forward to the projection system matrix for three-dimensional reconstruction of students learning a great help!)

水平集最新算法，基于局部能量函数，并做了改进(level set new Algorithm, make some improve)

Laplasian图像边缘提取，亲测有效，欢迎下载使用。(Edge extraction of Laplasian image)

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)

主要是实现图像的融合,是在梯度域里面进行一个图像的融合，(shi xian tu xiang de rong he，in tidu yulimian rong he ercheng ,matlab huan jing,xia ,keyi yunxing qieyou ceshi shuju d e.)

低秩的求解 denoise an image by sparsely representing each block with the already overcomplete trained Dictionary, and averaging the represented parts. Detailed description can be found in "Image Denoising Via Sparse and Redundant representations over Learned Dictionaries(low rankdenoise an image by sparsely representing each block with the already overcomplete trained Dictionary, and averaging the represented parts. Detailed description can be found in "Image Denoising Via Sparse and Redundant representations over Learned Dictionaries)