matlab_face_detection

使用Tensoflow实现图卷积神经网络模型(Use Tensoflow Implementation Figure convolution neural network model)

数字水印技术基于matlab的程序源码。。基于dct域的水印技术(Digital watermarking technology is based on the procedures for matlab source. . Based DCT domain watermarking technology)

基于HOG的行人检测，作者的源代码有错误，现代码已经进行了改变，并可以调试通(Histograms of Oriented Gradients for Human Detection, the writer s code has some errors, and I have correct the errors, and the code is right under C++ buidler)

图像处理效果评价,评价指标包括峰值信噪比，熵，以及均方误差，具体公式请参考相关文献(photo)

说明：  根据一幅杂草和作物混合的图像可以识别出图像中的杂草(Weeds in the image can be identified according to a mixed image of weeds and crops)

内含张正友，tsai的标定方法，可以求解摄像机内参数u轴尺度因子，v轴尺度因子，摄像机外参数：两摄像机坐标系原点之间的旋转矩阵和评议矩阵，可以得出摄像机像素参数，焦距，畸变系数，并且带有优化结果。(Containing Zhang Zhengyou, tsai calibration method that can solve the camera parameters u-axis scale factor v The axis scale factor, the camera extrinsic parameters: the rotation matrix between the two camera coordinate origin, and appraisal matrix can be drawn from the camera pixel parameters, focal length distortion coefficient, and with optimal results. )

说明：  通过获取摄像机内外参数获得双目视觉处理图像，得到视差图，得到深度图。(The binocular vision processing image is obtained by obtaining the camera's internal and external parameters, and then the disparity map is obtained, and then the depth map is obtained.)

本程序是用似然比检验的方法进行profile中的变点检测(This procedure is used likelihood ratio test method of change point detection profile)

本文介绍了支持向量机基于核方法的聚类算法及其应用(This paper introduced the support vector machine based on kernel method for clustering algorithm and its application )

三维激光点云配准是点云三维建模的关键问题之一。经典的 ICP 算法对点云初始位置要求较高且配准 效率较低，提出了一种改进的 ICP 点云配准算法。该算法首先利用主成分分析法实现点云的初始配准，获得较好 的点云初始位置，然后在经典 ICP 算法的基础上，采用 k － d tree 结构实现加速搜索，并利用方向向量夹角阈值去除 错误点对，提高算法的效率。实验表明，本算法流程在保证配准精度的前提下，显著提高了配准效率。 (Three-dimensional laser point cloud registration is one of the key three-dimensional point cloud model. High classical ICP algorithm to the initial position of the point cloud registration requirements and low efficiency, proposed an improved ICP point cloud registration algorithm. Firstly, the use of principal component analysis of the initial point cloud registration, get a better initial position of the point cloud, then the basis of classical ICP algorithm using k- d tree structure to achieve speed up the search, and using the direction vector angle the removal of the threshold point error and improve the efficiency of the algorithm. Experiments show that the algorithm processes to ensure the accuracy of registration under the premise, significantly improve the efficiency of registration.)