SIFTVC6

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.)

这是一个关于鲁棒的多传感器图图像配准程序的算法可直接使用。 (This is a robust multi-sensor chart image registration procedures of the algorithm can be used directly.)

基于visual c++平台，相机标定方法中的直接线性变换方法，即DLT方法(Based on visual c++ platform, camera calibration method of direct linear transformation method, namely DLT method)

用C++编写的实现GVF的活动轮廓模型的提取，值得你参考(Extracted with implementations written in C++ GVF active contour models, it is worth your reference)

对数字图像的处理、灰度化等，对数字图像进行基本的操作与处理(Basic digital image manipulation and processing of digital image processing, gray,)

说明：  对灰度图象进行正交变换,并进行滤波,产生平滑图象(Gray-scale images of the orthogonal transformation, and filtering to produce a smooth image)

一个小巧的图片水印工具，可在图片的指定位置写入文本，文本字体可调(A small picture watermark tool)

中科院图像理解与计算机视觉课件6 值得参考一下 some topics on pattern recognition and computer vision(Chinese Academy of Sciences and Computer Vision Image Understanding Courseware 6 worth refer to some topics on pattern recognition and computer vision)

说明：  物体运动的跟踪，比如行人和车辆等物体的跟踪(Tracking the motion of an object, such as a pedestrian or vehicle)

对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果(Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descriptor, then according to the results, the coarse matching results, using RANSAC method to calculate the fundamental matrix, and eliminate the error matching, gain a more accurate matching results)