摘要
提出了一种基于修正交叉视觉皮质模型(MICM)的图像自适应分割新方法.根据待分割图像的自身特性,自适应地设定参数,并以互信息量为目标函数选取最佳分割结果.该方法解决了针对不同的图像需要人工设定交叉皮质模型(ICM)参数和需要人工选取最佳分割结果的2个问题.实验结果表明,与通过大量实验获得模型参数的脉冲耦合神经网络(PCNN)基本模型和ICM基本模型相比,MICM与其综合评价函数值相近;与模糊聚类分割算法和最大类间方差(OTSU)算法相比,MICM算法有较明显的视觉优势,并且其综合评价函数值也分别提高了约15%和13%.
An image segmentation method based on the modified intersecting cortical model （MICM） is proposed to set the MICM parameters adaptively according to the different characteristics of images and choose the optimal segmentation results automatically, which are two main obstacles for the basic intersecting cortical model（ICM） to be used in practice. Experiments show that the comprehensive evaluation value of MICM is close to those of basic pulse coupled neural network（PCNN） and basic intersecting cortical model. Compared with the fuzzy C-means algorithm and OTSU algorithm, MICM is of visually better segmentation and the comprehensive evaluation value of MICM increases by approximately 15% and 13% respectively.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2010年第1期56-60,共5页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(60702031
60873241)
国家高技术研究发展计划项目(2008AA01Z217
2007AA01Z145
2007AA01A127)
关键词
图像分割
交叉视觉皮质模型
自适应
互信息量
image segmentation
intersecting cortical model
self-adaptive
mutual information
