搜索资源列表
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
SiftDetect
- shift特征检测用于浙大校徽检测的一个例子-shift feature detection test for an example of Zhejiang University Logo
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.
OpenCV
- OpenCV_人脸检测 SIFT特征匹配-OpenCV sift match
opencv-face-capture
- opencv人脸捕获，判断眼睛，嘴巴等特征是否有遮挡-opencv face capture,opencv face capture,opencv face capture,opencv face capture,opencv face capture,opencv face capture,opencv face capture,
OpenCV-Mean-Shift-demo
- 基于OpenCV实现的图像特征区域跟踪程序，基于类Mean-Shift实现的颜色直方图概率匹配算法，可同时跟踪6个特定目标区域。-OpenCV implementation of image features based on regional tracking program, based on class Mean-Shift probability of achieving the color histogram matching algorithm, which can track a
firedetect_CSHARP
- 火焰检测系统，通过火焰的静态颜色特征和动态的抖动特征去识别。-fire detection
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
track_color
- 基于颜色特征的rgbmeanshift跟踪算法，可以直接运行。-Based on Color Features rgbmeanshift tracking algorithm can be directly run.
cvgabor
- 这里包含高质量的Gabor实现代码，基于OpenCV。Gabor变换可以实现在多个尺度、多个方位上的变换，尤其是对于纹理的检测有很好的效果，研究表明Gabor特征符合人眼感受野特性。 -Gabor included here to achieve high-quality code, based on OpenCV. Gabor transform can be achieved in multiple scales, multiple orientations of the transform
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.
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
opencv_surf
- opencv提取surf特征点 并进行匹配(Extracting surf feature points using opencv)
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.)
代码 特征点匹配目标提取
- 利用OpenCV以及vc++实现对物体特征点匹配目标的提取，(Using OpenCV and vc++ to achieve object feature point matching target extraction,)
字符特征提取
- 结合OpenCV在VS2015平台上对含有字符特征提取，例如可以进行车牌识别等(Combined with OpenCV on the VS2015 platform to contain character feature extraction, such as license plate recognition can be carried out)
quickly match
- 基于亮度/色彩一致性，在SURF算法的基础上提出了一种快速图像特征点匹配算法，可以缩小特征点匹配所需的运行时间，提高正确匹配率。(Based on the brightness / color consistency, a fast image feature point matching algorithm based on SURF algorithm is proposed, which can reduce the running time of feature point matchi
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
5图像简单特征检测
- 利用opencv实现图像简单特征检测，可直接运行(Opencv realization of image feature detection)
venv
- OpenCv3.4+py3.4 实现特征点检测、匹配以及相机的标定(OpenCv3.4+py3.4 Realization of feature point detection, matching and camera calibration)