摘要
根据人头特征,提出了一种基于网格和密度的聚类算法。该算法将图像分成网格,然后逐行计算网格的密度,碰到符合密度要求的网格时,算法转为纵向计算网格的密度,记录下纵向符合密度要求的网格数量,以此判断是否存在人头以及计算人头的参数。该算法结合了网格聚类的低时空复杂度和密度聚类的良好抗噪性的特点。实验证明该算法速度比Hough变换快两个数量级,而且所需存储空间小。
According to the features of human head,the paper puts forward a new clustering algorithm based on grid and density.In the algorithm,image is divided into grids,then calculates the density of every grid line by line.When coming across a grid that meets the density requirement,algorithm turns to calculate the density of grid vertically,and records the number of vertical grids that meets the density requirement.Then judge whether there are human heads and calculate the parameters of head.This algorithm has the merit of grid-based clustering which is low-complexity in time and space,and has the merit of density-based clustering which is good noise immunity.The experimental results show that it is two orders magnitude faster than Hough transform,and requires small storage space.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第31期145-146,160,共3页
Computer Engineering and Applications
基金
上海市重点学科建设项目资助No.T0501
上海市科学技术委员会的资助(No.08210511100)~~
