摘要： 根据核函数的不同特点，分析得出均值移动算法的步长可能为牛顿步长、高斯-牛顿步长或一种非线性步长。根据均值移动密度函数泰勒展开形式的不同，讨论均值移动点每步在运动方向上的密度递增范围和密度极大值点的位置。上述结论对于提高均值移动算法的收敛速度有指导作用。

关键词: 均值移动, 图像处理, 目标跟踪, 收敛性

Abstract: According to the different characteristics of kernel functions, this paper concludes that the mean-shift step may be Newton-step, gauss Newton-step or nonlinear-step. The bound of the density increasing and the position of density maximum at every step are discussed according to the different Taylor series of the mean-shift density functions. The convergence speed can be increased based on such conclusions.

Key words: mean-shift, image processing, object tracking, convergence

中图分类号: 

• TP301.6