256-gray

使用KINECT获取深度图像的源码，在vs平台下使用C++编写。(Use KINECT obtain depth image source, written using C++ in vs internet.)

基于加权最小二乘最优化框架的滤波器。可用于实现图像的分层编辑。(Weighted least square)

delaunay三角化程序 delaunay三角化程序 delaunay三角化程序(delaunay triangulation delaunay triangulation process procedures delaunay triangulation process)

说明：  本代码采用的是opencv，有视频采集和对采集的视频帧进行laplace处理的功能！(This code uses a opencv, with video capture and the capture of video frames laplace processing capabilities!)

RAw格式图片的显示，边缘检测，包括SOBEL算子，具有不同模板的拉普拉斯算子边缘检测，以及一些简单的滤波方式(sadfsadf asdfasdf asdf)

VC计算机图形学原理及算法教程 VC计算机图形学入门教程(VC theory and algorithms of computer graphics computer graphics tutorial Tutorial VC)

本程序的细化算法要应用于含线条的黑白二值图像才可以看出细化效果。(This procedure should be applied to the thinning algorithm with two lines of black-and-white image before thinning effect can be seen.)

用光流法进行运动物体的检测和跟踪，并返回运动方向(Light flow method of moving object detection and tracking, and return to the direction of movement)

基于openvc平台的，用vc语言实现的，根据输入的图像，对图像进行腐蚀操作。(Based on openvc platform, with vc language, according to the input image, the image erosion operation.)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)