摘要
为了实现脉冲耦合神经网络(PCNN)在图像分割过程中模型参数与迭代次数的少量化,提出了一种改进的PCNN快速图像分割算法。算法对PCNN模型进行了简化,将传统PCNN模型中恒定的连接系数与神经元所在像素点的像素值联系起来,去除了PCNN分割图像过程中的人工设置参数过程,并根据图像灰度统计特性,将动态阈值转变为恒定阈值,仅一次迭代便可完成图像分割。实验结果表明:算法的分割结果主观视觉感受良好,时间复杂度低,优于对比算法。
In order to reduce the number of model parameters and iteration times of the Pulse Coupled Neural Network(PCNN)in image segmentation,an improved PCNN fast image segmentation algorithm is proposed.PCNN is simplified and the constant connection coefficient is linked with pixel grayscale value of corresponding neurons in the algorithm.The algorithm does not need to set PCNN parameters artificially in image segmentation and the dynamic threshold is transformed into constant threshold according to grayscale statistical characteristics of the image so that only one iteration time is needed for image segmentation.The experimental results show that the proposed algorithm has good performance of subjective visual perception and lower time complexity,and it is better than comparison algorithm.
作者
史红亮
SHI Hongliang(Faculty of Electrical and Mechanical Engineering,Kunming Metallurgy College,Kunming 650033,China)
出处
《昆明冶金高等专科学校学报》
CAS
2021年第1期86-89,共4页
Journal of Kunming Metallurgy College
基金
昆明冶金高等专科学校科研基金项目:基于智能控制策略的捣固车起拨道装置电液比例系统研究(2020XJZK03)。
关键词
图像分割
参数设置
PCNN模型改进
迭代次数
image segmentation
parameter setting
PCNN model improved
iteration times