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SIFT_VC opencv下的图像sift特征提取以及匹配跟踪识别
- opencv下的图像sift特征提取以及匹配跟踪识别-opencv image under sift recognition feature extraction and matching track
OrbTest
- ORB描述子，是比sift算法更加快速的图像特征点提取方法，检测的方法更加暴力。opencv已经实现。-ORB descr iptors, sift algorithm is faster than the image feature extraction methods, detection of the method is more violence. opencv has been achieved.
Image-feature-extraction
- 图像特征点提取，需要安装opencv2.1-Image feature extraction, need to install opencv2.1
FaceRecog_src100902
- 基于OpenCV的人脸识别演示程序。目前实现了Gabor+Fisherface算法，还有几何和光照归一化。 -->请到 http://code.google.com/p/facerecog/ 下载最新版本。<-- 功能：对摄像头拍摄的或用户指定的图像，检测其中人脸，然后在已存储的人脸库中找到最匹配的人脸并显示。 在VS 2008 SP1上编写，使用了OpenCV 2.0和MFC，通过消息处理函数与用户进行交互，利用多线程来实时显示图像。 数据处理分为了C
Adative-contour-extraction
- 在图像中提取轮廓（用到了自适应阈值分割算法），根据目标特征，如周长、面积等参数进行筛选，得到目标的轮廓。-Contour extraction in the image (use an adaptive thresholding algorithm), depending on the target characteristics, such as perimeter, area and other parameters of screening, target profile.
Gray-Level-Co-Occurrence-Matrix
- 基于OpenCV和VS2008的图像灰度共生矩阵特征提取实现。文件中还有matlab版本的-Images based on OpenCV and VS2008 GLCM feature extraction achieved. Matlab version of the file there
opencv_FeatureDetect_Test
- 这个是在刚学opencv时候的测试小代码。主要是图像特征提取方面的。同时，里面含有自编了canny、角点检测还有一些其他。里面每个文件均可以单独运行在VS20101平台下。（opencv2.1）-This is just a test when a small learning opencv code. The main aspects of the image feature extraction. At the same time, which includes self-compiled
motion-tracking-and-implementation
- 目标跟踪是计算机视觉的一个重要分支,日益广泛应用于科学技术、国防安全、航空、医药卫生以及国民经济等领域。实现目标跟踪的关键在于完整地分割目标、合理提取特征和准确地识别目标,同时,要考虑算法实现的时间,以保证实时性。当视频图像中被跟踪目标发生姿态变化,存在旋转或部分遮挡时,简单的灰度模板或者Hausdorff距离匹配一般很难达到实时跟踪目标的要求,出现误匹配或者跟踪丢失的情况,而且跟踪效率较低。Gary R.Bradski提出的CAMSHIFT[1](Continu-ously Adaptive
canny-jiance
- 对图像边缘检测，实现对图像特征的提取，对图像处理的下步工作做准备-Image edge detection
Test
- 在VC6环境下使用opencv来读取图像及提取特征，装上opencv，配置好环境变量就可用。-extrace feature
sift2
- sift算法，VC++ 6.0 开发，用于图像特征点提取，利用Opencv库进行开发-sift algorithm, VC++ 6.0 to develop for the image feature point extraction, Opencv library development
LBP
- 图像LBP特征提取，结合OpenCV2.3，LBP特征在图像检索方面用途很大。-The images LBP feature extraction, combined with OpenCV2.3 of LBP characteristics in image retrieval purposes.
tt
- OPENCV实现颜色识别，特征提取，运动检测等功能，其中包含读取图像-OPENCV color recognition, feature extraction, motion detection function, which contains the read image
MyOpenCVConsole
- 常用的opencv图像特征提取算法。包括直方图、边缘检测、角点检测、直线提取、圆形提取等。并包括不太成熟的BP神经网络识别纯底色数字。-Commonly used opencv image feature extraction algorithm. Including histograms, edge detection, corner detection, line extraction, circular extraction. And includes less mature pure b
sift
- sift图像特征提取 vs2010+opencv2.2 如果想搞清原理，请参照如下内容： http://blog.csdn.net/zddmail/article/details/7521424 -sift image feature extraction vs2010+ opencv2.2 If you want to find out the principles, please refer to the following: http://blog.csdn.net/z
ConsoleApplication5
- vs 2013, opencv2.4.8 实现的图像特征提取与匹配，可以直接运行！-vs 2013, image feature extraction and matching opencv2.4.8 achieve, you can directly run!
LBP
- 利用Opencv实现LBP算法，完成图像特征提取-LBP algorithm utilizing Opencv achieve complete image feature extraction
feature-extraction-based-on-opencv
- 基于Opencv的图像特征提取源码整理。-feature extraction
code1
- 特征点提取及图像匹配,局部图像特征提取匹配(Feature point extraction and image matching, local image feature extraction matching.)
特征检测
- 图像特征提取是计算机视觉和图像处理中的一个概念。它指的是使用计算机提取图像信息，决定每个图像的点是否属于一个图像特征。包括：Harris角点、ShiTomasi角点、亚像素级角点、SURF角点、Star关键点、FAST关键点、Lepetit关键点等等(Image feature extraction is a concept in computer vision and image processing. It refers to using a computer to extract imag