vc_draw

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.)

基于自动种子选择的区域生长法，克服了传统区域法的很多缺点(Region growing method based on automatic seed selection, to overcome the many shortcomings of the traditional regional law)

对图像进行纹理处理，为图像识别、目标跟踪创造条件。主要分析：惯性矩、熵以及局部平衡性。(The image texture processing for image recognition, target tracking and create conditions. Main analysis: moment of inertia, entropy, and local balance.)

能够计算傅里叶描述子，并对图像进行重构，实现对目标图像和源图像的辨认(Fourier descriptors can be calculated, and the image reconstruction, image and the source image to target identification)

打开bmp位图文件 彩色转灰阶 直方图均衡 直方图规定 灰阶线性化(Open the bmp grayscale bitmap image binarization functions connected domain extraction features)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

连续投影算法的Matlab程序 下载后可以直接使用(Successive projections algorithm )

深度图像的快速实现增强的算法，效果非常好，但是速度很快(Quick to realize the depth of image enhancement algorithms, the effect is very good, but fast)

说明：  能精确的提取数字图像的轮廓。不管图形的形状如何，都能达到预期的效果。并且能将边界的坐标输出到一个文件中，供使用者分析数据。(Able to extract precise contours of the digital image. Regardless of the shape of how graphics can achieve the desired effects. The coordinates of the border and is capable of output to a file, for users to analyze data.)

此程序可以根据一幅图像的DEM读取一幅图像(This program can read an image of an image based on DEM)