1) pixel grayscale classification
灰度归类
1.
Firstly,the background image is extracted in the RGB space by improving pixel grayscale classification,and is updated real time with selective update and background adjustment.
该方法首先在RGB空间对像素灰度归类法进行了改进,并用其提取了背景图像,同时结合选择更新和背景调整来实时更新背景;然后对背景差分图像的RGB灰度之和,通过设定阈值来提取运动区域;最后对提取的目标阴影混合区在HSV空间,分别进行自上向下、自左向右及其反方向的色调、亮度及边界交叉点判别,以实现阴影检测和消除。
2) pixel intensity classification
像素灰度归类
3) intensity normalization
灰度归一化
1.
In this paper, a novel pretreatment approach of intensity normalization based on polynomial least squares fitting has been implemented to match the intensity between the images, and an accelerating algorithm for adaptively adjusting the deformation force of the floating image during the iterative process has been designed to speed up the process of convergence.
针对这两个问题,提出了一种基于多项式最小二乘拟合的灰度归一化方法对灰度进行匹配,并在迭代过程中自适应调整浮动图像所受作用力以加快收敛速度。
4) gray-like image
类灰度图
1.
A new concept of gray-like images is put forward.
在Viola-Jones快速目标检测算法的基础上,侧重研究了类Haar特征原型的本质与提取,提出了类灰度图的概念,并以快速人脸检测为例,从类灰度图提取广义类Haar特征,从本质上拓展了类Haar特征的类型。
5) gradient category
灰度分类
6) intensity cluster
灰度聚类
补充资料：归类
归类
同类相近的疾病，药物等归纳在一起并加以区分的方法。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条