摘要
脉冲耦合神经网络(PCNN)是20世纪90年代形成和发展的一种新型神经网络,在图像处理领域得到广泛的应用。本文提出了一种基于简化的PCNN与类内最小散度类间最大方差相结合的自适应图像分割方法,在每次迭代时将脉冲耦合神经网络点火的神经元对应的像素作为目标,未点火的神经元对应的像素作为背景,计算目标和背景像素灰度值的类间方差与类内散度,取类间方差与类内散度比值最大的分割图像作为最终结果。实验结果表明,本文算法可以有效地对不同图像进行自动分割,是一种可行的与有效的图像分割方法。
Pulse Couple Neural Network（PCNN） is a new Neural Network which is formed and developed in the 1990＇s and shows highly applicable in the field of image processing. This paper proposes a new automatic image segmentation method based simplified PCNN and the maximum value of the ratio of scattered measure within clusters and the between-cluster variance. The fired nerves and the unfired nerves of PCNN corresponding to pixels of image are considered as target and the background respectively. The within clusters and between-cluster variance are calculated at each process of iteration. The optimal segmentation result is obtained when the maximum value of the ratio of scattered measure within clusters and the between-cluster variance is achieved. Experimental results show that the method can achieve better image segmentation and has a common applicability.
出处
《自动化与仪器仪表》
2008年第6期97-99,共3页
Automation & Instrumentation
关键词
脉冲耦合神经网络(PCNN)
类内散度
类间方差
图像自动分割
Pulse coupled neural network（PCNN）
Scattered measure within clusters
Between-clusters variance
Automatic image segmentation
