搜索资源列表
myStructuredLight
- 这是从国外网站下载的源代码，实现了利用投影设备实现三维重构，从标定，到特征线提取，以及最后的三维重建均是很完整的，对于做三维重建的人是不可多得的资料。在这里不由得再次感叹老外的慷慨，希望大家也将自己的好源码共享。-This is from foreign web site the source code, and the realization of the projection equipment realize 3 d reconstruction, from the calibration
Qt_RobHess_Sift
- sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift
a
- OPENCV的SURF特征点提取源码,可以用于特征焦点的提取，运行于VS2010环境下通过-SURF feature points extraction of OPENCV source code, can be used to extract features of focus, running in VS2010 environment through
TestSift_match
- 基于SIFT的特征点提取和匹配,在源码基础上修改。-Extraction and matching based on SIFT feature points
lkdemo
- 光流法源码，实现lkdemo的实例程序并对每一帧画面都进行特征点检测。-Optical flow source to achieve lkdemo example program and each frame is the feature point detection.
LBP
- LBP特征特诊算子源码，用于求取图像8,16邻域的特征值，基于OpenCV-LBP features special diagnostic operator source code, for the image of the 8,16 to get the image of the characteristics of the neighborhood, based on the OpenCV
OpenCV-Features-Comparison-master
- opencv比较特征描述子性能测试源码，opencv2.4.9+vs2013-OpenCV Features Comparison,opencv2.4.9+vs2013
feature-extraction-based-on-opencv
- 基于Opencv的图像特征提取源码整理。-feature extraction
opensift-master
- open cv的sift尺度不变特征变换C代码源码，效果还不错，分享给大家学习-open cv sift the scale invariant feature transform source code for C, the results were good, for everyone to share learning
surfdescriptor
- 获取特征点后，对其进行描述。可用于降维处理。(After obtaining the feature points, it is described. It can be used to reduce dimension.)
boxfilter
- 用盒状滤波器对积分图像进行滤波，在进行特征点检测，大大减少了时间。(Using the box filter to filter the integral image, the feature points are detected and the time is greatly reduced.)
kaze-master
- Kaze的实现源码，其中包括KAZE的核心算法库以及KAZE特征的提取、匹配和比较等例程，是基于OpenCV实现的。(Kaze source code, including KAZE core algorithm library and KAZE feature extraction, matching and comparison routines are based on OpenCV.)