摘要
针对传统Otsu算法只用于单阈值分割的不足,将Otsu算法推广到多阈值彩色图像分割中,提出先在众多极大值中寻找有意义峰值,根据峰值将直方图划分成多个待分割区间,再在每个区间进行阈值选取的方法;并且综合运用了形态学的方法对分割结果进行优化,降低阈值法因不考虑图像空间特性而造成的对噪声敏感的影响。实验结果表明,该方法能自动而快速地对彩色图像进行多阈值分割,而且具有较强的抗噪能力。
As it is of deficiency that the conventional Otsu algorithm is only applicable to single-threshold,the algorithm is extended to multi-threshold color image segmentation.Firstly,the selection of meaningful peak values among a group of maximum values is carried out.Then,according to the peak values,the diagram is divided into a certain number of segmentation intervals,in which the threshold is selected.Based on methodology of morphology,the segmentation is optimized.And the influence,as a result of ignoring the characteristics of space,which the threshold technique imposes on noises,is lessened.The results of the experiment indicate that the Otsu algorithm based on multi-threshold color image segmentation is able to implement automatic and rapid multi-threshold segmentation,and is noise-resistant to some extent.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第11期176-178,共3页
Computer Engineering and Applications
基金
航空科学基金(No.20070718001)
关键词
颜色量化
多阈值
有意义峰值
形态学
color quantization
multi-threshold
the meaning peak
morphology
