RRT4

实现3D自动划分网格，网格包括三角形、四边形和六面体等(This function is for 3D automatical meshing.)

通过使用MATLAB语言建立三维渐开线齿轮啮合模型(use MATLAB to draw 3-D gear)

说明：  提取图像中的显著对象部分，就是人视觉感知的主要部分，运行的是demo.m(Extraction of the image part of a significant object is the main part of the human visual perception, running demo.m)

说明：  二代提升法实现的haar小波变换程序 及整数小波变换。haar小波实现(Method II upgrade the haar wavelet transform procedures and integral wavelet transform. Haar Wavelets)

根据教程自己写的对单目相机的标定，以及对一张镜像畸变图像的矫正，最后进行二维测距，根据像素值测得一条平面曲线的真实长度(According tutorials to write their own right monocular camera calibration, as well as a mirror image distortion correction, the final two-dimensional distance, measured according to the pixel values ​ ​ of the true length of a plane curve)

Gabor小波提取特征一幅输入的图形进行特征提取基于gabor小波变换 (Gabor wavelet feature extraction of an input graphics gabor feature extraction based on wavelet transform)

通过霍夫变换，实现延直线上信号的积累，达到提高信噪比的目地，以利于检测。(By Hough transform, the accumulation of signals in the certain straight line, to improve the SNR of the signal.)

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)

一种属性离散化的方法，对粗糙集适用(An attribute discretization method, the rough set for)

用于计算在三维点云处理过程中的局部点云面积估算，精度很高。(Used to calculate the three-dimensional point cloud processing local point cloud area estimation accuracy is very high.)