摘要
提出了一种结合灰色关联度和模糊熵的分割算法。传统模糊熵分割算法的隶属度函数只利用了图像灰度值的统计信息,因此算法容易受噪声或光照不均的影响。在隶属度函数的计算中,引入灰色关联度来表示像素的空间信息,能够更加准确地反映像素属于目标或背景的影响。由理想目标或背景点组成参考序列,待处理像素及其邻域组成比较序列,计算比较序列与参考序列之间的灰色关联度,并修正隶属度函数。对实际图像的测试实验表明,结合灰色关联分析的最大模糊熵分割算法比传统模糊熵分割算法具有更强的噪声抑制能力和更准确的分割结果。
A new image segmentation method based on grey relational analysis and fuzzy entropy is presented. The traditional fuzzy entropy methods are sensitive to the noise, because they only consider the statistical information of the gray values. In the proposed method grey relatinal degree is introduced to demonstrate whether the pixels belongs to object or background more accurately. The current pixel and its neighbors are selected as a comparative sequence, and the grey relational degree between the comparative sequence and the reference sequence is computed,based on which the membership function of the fuzzy entropy function is redefined so that the membership of the current pixel is determined by its own gray value and the gray values of its neighbor pixels. The segmentation experimental results of several real images exhibit the good performance on reducing the noise by the proposed method.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2008年第9期1250-1253,共4页
Journal of Optoelectronics·Laser
基金
航空科学基金资助项目(04D52032)
江苏省自然科学基金资助项目(K2001047)
关键词
图像分割
模糊熵
灰色关联度
image segmentation
fuzzy entropy
grey relational degree
