摘要
根据广义模糊集(GFS)理论,给出了用于模糊增强图像区域对比度的线性广义模糊算子(LGFO),从而给出了基于GFS的双线性快速模糊增强图像边界检测新算法.首先利用线性左半梯形隶属函数将灰度图像的普通集合变换为GFS,其次利用LGFO对GFS进行区域对比度增强,同时把GFS变换为模糊集合,然后再把模糊集合变换成普通集合,最后在普通集合中进行边界提取.通过大量实例证明,使用该算法提取图像边界速度快、效果好,而且多项指标均超过了文献[2]～[5].
According to the theory of generalized fuzzy sets (GFS), this paper gives the linear generalized fuzzy operator (LGFO) of fuzzy enhancement of contrast among successive region,then puts forward the new bilinear fast algorithm of fuzzy enhancement image edge detection. First the normal sets of grey image is translated to correspondent GFS by using left semi-trapezoid membership function, second proceeds fuzzy enhancement of contrast among successive region to GFS by using LGFO, at the same time, the GFS is translated to fuzzy sets, and then translates fuzzy sets to normal sets, at last the edge is extracted for normal sets. Through a great deal of instance checked, by using this algorithm to extract edge is fast for velocity and good for effect, all exceedes literature [2] ~ [5] on multi-index.
出处
《计算机工程》
CAS
CSCD
北大核心
2004年第19期6-7,共2页
Computer Engineering
基金
国家重点实验室基金资助项目(51473080101)
关键词
边界检测
广义模糊集
线性广义模糊算子
Edge detection
Generalized fuzzy set
Linear generalized fuzzy operator
