搜索资源列表
GetContoursAreaLengthMeanVari
- 图形图象中计算轮廓线总长度,均值和方差等特征,基于OpenCV来实现的.-graphic images to calculate contour length, the mean and variance characteristics, based on OpenCV to achieve.
LocalOstu
- 基于opencv的otsu阈值分割,根据最大类间方差原理,进行全局图像分类-Opencv based on the Otsu threshold segmentation, in accordance with the principle of maximum between-cluster variance for the overall image classification
Var
- 用OpenCV编写的求图像方差程序，可用于图像处理-Written request with the OpenCV image variance procedures, can be used for image processing
ground_detect
- 基于OpenCv的背景检测，先要建立背景模型，检测在背景下的物体。（用的是均值方差的方法建立背景模型）-Background detection based OpenCv to first build the background model to detect objects in the background. (Using a mean-variance method to establish the background model)
zhifangtu
- 利用opencv对视频进行灰度直方图处理，并求出均值，方差。。。 -Using opencv histogram processing for video, and find the mean and variance. . .
MinVarianceFilter
- 基于OpenCV，灰度最小方差算法在图像模糊中的应用，仅头文件，方便调用-Min-Gray-Variance algrithom in digital image processing based on OpenCV,just header file,you can call it conveniently.
Otsu
- 最大类间方差法实现二值化，基于VC++平台的图像处理基本方法，使用OPENCV进行数据操作-The maximum between-class variance method to achieve binarization, VC++ platform-based image processing of the basic method of use OPENCV data manipulation
sauvola
- 使用sauvola方法进行的图像二值化，其中包含了计算局部均值和方差的函数，使用Opencv进行数据保存-Use sauvola method of image binarization, contains a function to calculate the local mean and variance, using Opencv for data storage
secaizhuanyi
- 运用openCV编写的C++图像色彩转移程序，原理是先转化到Lab图像，再保持L不变调整图像的a、b分量的均值跟方差。-Using openCV prepared C++ image color transfer process, the principle is first converted to Lab image, and then adjust the image to keep L constant a, b, with the mean variance component.
opencv
- 运用OPENcv来求解图像方差，可以对任意格式的图像进行求解-Use OPENcv to solve image variance, can be solved in any format image
hist
- 用OPENCV辅助，检测图像的直方图，并画出直方图，统计直方图的均值和方差，并输出颜色占比例最大的值。-Histogram OPENCV assisted detection image and draw the histogram, histogram mean and variance, and the largest proportion of the output color value.
pca
- 在许多领域的研究与应用中，往往需要对反映事物的多个变量进行大量的观测，收集大量数据以便进行分析寻找规律。多变量大样本无疑会为研究和应用提供了丰富的信息，但也在一定程度上增加了数据采集的工作量，更重要的是在多数情况下，许多变量之间可能存在相关性，从而增加了问题分析的复杂性，同时对分析带来不便。如果分别对每个指标进行分析，分析往往是孤立的，而不是综合的。盲目减少指标会损失很多信息，容易产生错误的结论。 因此需要找到一个合理的方法，在减少需要分析的指标同时，尽量减少原指标包含信息的损失，