摘要
当前航空安全形势紧张,所有国家都正在加强其安全检测系统。鉴于危险物品的特殊性,对含有危险物品的行李包的X射线图像进行模拟是一种有效培训安检人员的方式。这种模拟方式可以增强安检人员对不同背景环境下危险物品X射线特征的敏感度。在本文中,首先对X射线图像融合的物理过程进行了分析并且介绍了当前此领域中普遍使用的融合算法,随后采用三层前馈神经网络模型对物理过程进行仿真从而实现X射线图像融合的目的,并与传统融合算法进行了实验比较。结果表明对于X射线图像融合,神经网络模型在融合精度及效果方面有极大的优势。
Under the current urgent circumstances of the aviation security,all countries are intensifying the security inspection.In the view of the specialty of dangerous items,simulating the X-ray image of luggage with dangerous items is an effective way to train inspectors.This simulating method could strengthen their sensibility to the X-ray image of dangerous items in different background.In this paper,firstly analyzing the physics process behind X-ray images fusion and introducing the traditional fusing algorithm,then constructing one three layers neural network to simulate the physics process and achieving the fusion aim,in the end,comparing the neural network algorithm and traditional algorithm.It＇s clear that the neural network model possesses great advantages for X-ray images fusion in the precise and effect aspect.
出处
《CT理论与应用研究（中英文）》
2011年第2期235-243,共9页
Computerized Tomography Theory and Applications
基金
国家自然科学基金(10705018)
