mnfcemm

Teach you the pipeline of 3D object, especially the perpective project

说明：  在图像上画一条线，提取所在位置的像素。主要是用来观察横向异质性变化的。(Draw a line on the image and extract the pixels at the location. Mainly used to observe changes in lateral heterogeneity.)

能够生成各种用于结构光的条纹，可供选择与使用(It is possible to generate various stripes for structural light)

模拟小孔或者圆形柱体结构的三维仿真图像，白光入射下的散射分析(Three-dimensional simulation image simulation holes or circular cylindrical structure, under incident white light scattering analysis)

自然人机交互的示例源代码，虚拟一个3D人，受显示中人的控制(Natural machine interaction sample source code, a 3D virtual person, by the display control of the human)

说明：  基于dwt的数字水印源代码,可以进行水印的嵌入与提取(Dwt-based Digital Watermarking source code can be embedded and extracted watermark)

点云数据集，用于点云数据的学习实验，wrl格式，可以用meshlab软件打开使用(Point cloud data set for point cloud data learning experiment, wrl format that can be opened using software with meshlab)

FLAC3D声发射模拟代码，可以模拟声发射的过程，记录凯赛效应(FLAC3D acoustic emission Codes)

Post process with irrlicht Engine

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)