搜索资源列表
OPENCV_GAUSS
- 基于opencv的高斯背景提取法，包括源代码和注释-Opencv-based Gaussian background subtraction method to extract
SimplestSaliencyExample
- 基于OPENCV的前景目标分割程序，使用高斯背景建模将移动的前景目标分割出来。-a saliency region extraction program based on OPENCV library using Gaussian background modeling method.
Gaussian-modeling-approach
- vc++和opencv,运用高斯建模的方法对场景进行背景建模，背景差分。从而检测出运动的物体，如机动车辆、行人，烟火等。-vc++ and opencv, using the method of Gaussian modeling scene background modeling, background difference. To detect moving objects, such as motor vehicles, pedestrians, fireworks, etc.
ga
- 基于混合高斯模型的背景建模方法的实现，用C++语言编写-Gaussian mixture model-based background modeling method of implementation, with C++ language
GMMS
- OPENCV下基于高斯混合模型的图像分割，程序中还有 基于大津法的图像分割和金子塔分割。-OPENCV Based on Gaussian mixture model of image segmentation, the program also includes Otsu method based on image segmentation and the segmentation pyramid.
YCbCrGauaaModel
- 利用基于YCbCr空间的高斯模型建立背景，并实时更新，通过背景差分法检测出道路中的运动车辆目标，效果较好。-Gaussian model based on the YCbCr space is used to establish the background, updating in real time. Then background subtraction method is used to detect the target of the movement of vehicles in
GaussBGModel
- 用opencv实现的运动目标检测算法，程序用的是高斯背景建模法，测试用的是夜间车流量的视频，光线变化不大，效果不错-Moving target detection algorithm, with opencv program with the Gaussian background modeling method, the test is the video of night traffic light changes, the effect is good
expectaionMaximation_CPP
- 通过gaussian mixture Model 产生一组点， 通过 Expectation Maximation方法对这些等进行划分。 结果表明，此算法能够很好的确认gaussian mixture model。-Gaussian mixture Model is used to produce a set of points. And through Expecation Maximation Method the points will be classified. The results
Gaussian1.0
- 一种由高斯分布公式入手的高斯噪声的添加方法-A Gaussian distribution formula Gaussian noise added to start method
classifer
- 二分类问题采用包括逻辑回归、最小二乘法、感知器算法（按下space不断迭代）、svm线性分类，另外还有高斯分线性分类（待完善），针对平面上两类点进行分类-Second classification using logistic regression, the method of least squares, perception algorithm (Press space iteratively) svm linear classification, in addition to the Ga
gaosi
- 基于混合高斯背景建模的方法用于检测场景中的运动车辆，采用Visual C++和OpenCV实现，程序有详细注释，并且附带测试视频，希望对大家有帮助。-The movement of vehicles, based on Gaussian mixture background modeling method for detecting the scene to adopt the Visual C++ and OpenCV realization, procedures detailed note
detect-motion
- 在VC++6.0的MFC中结合opencv1.0的环境中编写的运动目标检测，采用的是背景差分法，混合高斯背景建模-Combined opencv1.0 environment written in the MFC VC++6.0 moving target detection using background subtraction method, Gaussian mixture background modeling
Lsm_match
- 最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。-Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matchin
guassian
- 混合高斯背景模型，背景差法，检测运动目标，环境VC2008，Opencv，前景与背景分开显示-Gaussian mixture background model background subtraction method to detect moving targets
Gaussian-Blur
- This project for OpenCV C++. This is the most commonly used smoothing method. This is also used to eliminate noises in an image. Gaussian kernel is slided across the image to produce the smoothed image. Size of the kernel and the standar
fastDetectLine
- VS2010下用opencv2.3.1编写的光条提取算法，机器视觉中用的多，本方法使用了高斯滤波和hession矩阵，经过对高斯滤波函数的处理，使得本方法的提取速度大大提升，提取精度是亚像素级别，附件内含测试图片-Under VS2010 extracted with light bar and more opencv2.3.1 written algorithms used in machine vision, this method uses a Gaussian filter and he
12354
- 实现了多高斯建模法的视频分割算法和越界检测、运动物体尺寸检测、计数等应用。算法主要由OPENCV实现。 软件目前可实现以下功能： 1）提供高斯建模法研究相应算法实现的效果的影响； 2）可以实现原视频与处理后的视频同时播放，实现跟踪； 3）实现车流量技术计数。 -To achieve a multi-Gaussian modeling method of video segmentation algorithm and cross-border detection, movin
loci
- 实现用帧差法画质心轨迹 高斯背景建模 可以运行的程序 opencv-Picture frame difference method implemented by the loci of Gaussian background modeling
optical-flow-
- opencv中高斯金字塔光流法，可以得到物体运动时的光流图像-opencv Gaussian pyramid optical flow method, optical flow image of the object can be obtained during exercise
Gussian
- 基于混合高斯模型的背景减除法，用于分离前景和背景-Background subtraction method based on Gaussian mixture model for separating the foreground and background