machine-vision-algorithm-

这MFC图像处理源代码总集包括了均值滤波，直方图均衡，高斯平滑，，锐化，拉普拉斯锐化（边缘检测），阈值变(With the MFC implementation of an image processing example code)

说明：  利用vc画图，特点是可以画一些有特殊效果的几何图像(use vc drawing, painting features can be some special effects of geometric images)

这是一个基于vc++语言下读取像素点灰度值(write highhui of a photo!)

openCV+MFC（VS2008）canny边缘检测，能用滑动条调节参数(openCV+MFC (VS2008) canny edge detection, use the slider to adjust parameters)

在图像中加入椒盐噪声的源代码，编译后的可执行文件须带参数运行(the image Salt and Pepper to the noise source, the compiled executable file parameters required to bring Operation)

针对粒子概率假设密度(PH D)滤波算法在虚警、漏检情况下，b标状态估计不稳定和b标可观测性车弱的问题，提出了一种基于序贯融合的粒子PH D滤波方法，利用雷达和红外传感器多目标进行融合跟踪杏基本思想是先对红外传感器进行粒子PH D滤波，丙将红外传感器滤波结果作为雷达的预测值，然后利用P达观测的数据进行更新，这样通过雷达和红外传感器交替上作保证目标状态的可观测性，从滤波器输出结身即可得到目标的状态信息仿真结果表明，在虚警、漏检和密集目标环境下，该方法是有效的和稳健的(The problem of target state estimation instability and observability weakerin the presence offalse alarms and missed detection was deal with. On the basis of sequential fusion, a particleprobabil- ity hypothesis density(PHD) filter for multrsensor multrtarget tracking was proposed. Observed da to collected from the infrared sensor was estimated with the particle PHD filter. Then the results from the filter were set as the radar predicted value by the radar observations. The multrtarget state can be updated to guarantee observing the target state. In this way, the global state is updated at the fusion center. Simulation results show that the proposed algorithm is effective and robust under the false warning, omission and concentrated target environment. )

说明：  解决一些图像处理的小问题，具体的划分和问题，文件里有提到(Solve some small problems in image processing)

主要是用于雷达目标跟踪中的数据融合，有很好的作用(It is mainly used for data fusion in radar target tracking.)

imageReadAndShow2.cpp,读取asc文件,并在VTK中渲染显示...(read ASC File,using VTK)

说明：  很好用的三维重建软件，VisualSFM软件允许上传一系列图像，它从这些图像中找到每一个图像的特定特征，利用这些特征信息重建出3D模型的稀疏点云，而后还可进行稠密点云重建。(The VisualSFM software, a good 3D reconstruction software, allows uploading a series of images. It finds the specific features of each image from these images, and USES these feature information to reconstruct the sparse point cloud of the 3D model, and then it can reconstruct the dense point cloud.)