The kernel based tracking has two disadvantages： the tracking window size cannot be adjusted efficiently, and the kernel based color distribution may not have enough ability to discriminate object from clutter backgr...The kernel based tracking has two disadvantages： the tracking window size cannot be adjusted efficiently, and the kernel based color distribution may not have enough ability to discriminate object from clutter background. For boosting up the feature＇s discriminating ability, both scale invariant features and kernel based color distribution features are used as descriptors of tracked object. The proposed algorithm can keep tracking object of varying scales even when the surrounding background is similar to the object＇s appearance.展开更多
高等数学里给函数项级数sum from m=1 to ∞a_n的和的定义: 若级数sum from m=1 to ∞a_n的部分和数列S_n极限存在,即 则称级数收敛,S称为级数sum from m=1 to ∞a_n的和。 定义本身已给出了收敛级数求和的方法。但求出级数的和,却是一...高等数学里给函数项级数sum from m=1 to ∞a_n的和的定义: 若级数sum from m=1 to ∞a_n的部分和数列S_n极限存在,即 则称级数收敛,S称为级数sum from m=1 to ∞a_n的和。 定义本身已给出了收敛级数求和的方法。但求出级数的和,却是一件比较困难的事情。为了帮助同学们掌握一些最基本的方法和技巧,我把级数sum from m=1 to ∞a_n的求和问题,分成以下几种情况。展开更多
基金the National Natural Science Foundation of China under Grant No.60775022
文摘The kernel based tracking has two disadvantages： the tracking window size cannot be adjusted efficiently, and the kernel based color distribution may not have enough ability to discriminate object from clutter background. For boosting up the feature＇s discriminating ability, both scale invariant features and kernel based color distribution features are used as descriptors of tracked object. The proposed algorithm can keep tracking object of varying scales even when the surrounding background is similar to the object＇s appearance.
文摘高等数学里给函数项级数sum from m=1 to ∞a_n的和的定义: 若级数sum from m=1 to ∞a_n的部分和数列S_n极限存在,即 则称级数收敛,S称为级数sum from m=1 to ∞a_n的和。 定义本身已给出了收敛级数求和的方法。但求出级数的和,却是一件比较困难的事情。为了帮助同学们掌握一些最基本的方法和技巧,我把级数sum from m=1 to ∞a_n的求和问题,分成以下几种情况。
