摘要
将神经网络思想引入到景象匹配,提出了基于模糊集的神经网络景象匹配算法。该算法将图像模糊集作为特征空间,尝试了在模糊域中采用3层前向神经网络搜索算法进行精确寻优的方法。实验结果表明,设计的算法不但较好地满足了景象匹配系统对匹配概率和匹配精度的要求,而且比传统算法具有更高的抗干扰能力,且实时性也较好,为飞行器实时景象匹配提供了一种可行方法。
Via introducing neural network theory and fuzzy sets theory , a novel neural network algorithm （NNBF） based on fuzzy sets is proposed. Image fuzzy sets are taken as feature space, and the exact positions are optimized by using 3 layers forward neural network algorithm. Experimental results show that the proposed algorithm is more advantagous in matching accuracy, speed, robustness, and has anti-jamming performance compared with conventional methods. It is fit for the real-time aircraft scene matching.
出处
《红外与激光工程》
EI
CSCD
北大核心
2007年第4期543-546,共4页
Infrared and Laser Engineering
基金
国防十五重点预研资助项目(413220209)
关键词
景象匹配
匹配算法
神经网络
模糊集
Scene matching
Matching algorithm
Neural network
Fuzzy sets
