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说明：  基于c#运动控制及图像处理项目源码，有兴趣可以看看(Based on C# motion control and image processing project source code)

分块计算图像的纹理，包括均值，标准偏差和平滑度等(The texture of a image is calculated based on blocks,including mean, standard deviation and smoothness and so on. )

对于MATLAB双目视觉处理中，通过前期获取摄像机参数等后制成深度图。(For MATLAB binocular vision processing, we can get the depth map after obtaining camera parameters in the early stage.)

人脸图像配准，将同一场景的两幅或多幅图像进行对准，可以实现人脸归一化(Face image registration, the same scene two or more images for the alignment can be achieved face normalization)

demons算法用来实现图像的配准，特别是医学图像的配准，已经达到了很高的精度(demons algorithm is used to achieve image registration, especially in medical image registration, has reached a very high accuracy)

对图像进行分块处理，可以设置分块比例，该程序设置的是8*8的比例，将图像分成64块(The image processing block, you can set the ratio of the block, the program is set proportion of 8* 8, the image is divided into 64)

图形图像处理，混合高斯图像背景估计＆更新(Graphic image processing, mixed-Gaussian background image is estimated )

对输入的图像帧感兴趣区域进行逆透视投影变换，得到俯视图。(The input of the image frame of interest in the area of the inverse perspective projection transform, get a top view. InversePerspectiveMapping)

该程序主要功能是实现两幅图像特征点的匹配。首先用harris角点检测算法检测出图像角点，然后使用ssd算法筛选匹配的角点，最后将匹配的角点用线连接起来(The program is to achieve two main functions of image feature points of the match. First, with harris corner detection algorithm to detect image corners, then use the ssd corner matching algorithm selection, the final match of the corner will be connected with a line)

医学图像配准，医学图像2D_3D配准，基于vc++开发。(The registration of pre-operative volumetric datasets to intra-operative two-dimensional images provides an im- provedway of verifying patient positionandmedical instru- ment location. In applications from orthopedics to neuro- surgery, it has a great value in maintaining up-to-date in- formation about changes due to intervention. We propose a mutual information-based registration algorithm which es- tablishes the proper alignment via a stochastic gradient as- centstrategy. Ourmaincontributionliesinestimatingprob- ability density measures of image intensities with a sparse histogrammingmethodwhichcouldlead to potentialspeed- up over existing registration procedures and deriving the gradient estimates required by the maximization procedure. Experimental results are presented on fluoroscopy and CT datasets of a real skull, and on a CT-derived dataset of a real skull, a plastic skull and a plastic lumbar spine seg- ment.)