【摘要】 构建了马尔可夫随机场自适应邻域系统,并将有形目标检测问题构建为马尔可夫随机场理论框架下背景与目标的二元分类问题.首先分析了影响目标形状的主要因素,归纳总结了典型的目标形状;其次以典型目标形状为模板构建了新的马尔可夫随机场邻域系统;然后构建了自适应邻域选择的代价函数,并基于有限差分算子创建了新的马尔可夫随机场势函数,进行背景与目标的分类判别.由于采用自适应邻域系统,所提算法在保持目标检测率的同时进一步降低了过门限率;比经典马尔可夫随机场邻域系统具有更好的目标形状保护新能.仿真试验结果表明,所提算法不仅具有较好的目标检测性能,而且可更好地保护目标形状的细节信息.更多还原

【Abstract】 The problem of shape target detection was formulated as a binary classification problem of each pixel under Markov Random Field(MRF)theoretical framework and the adaptive neighborhood system of MRF was introduced.Firstly,the factors that cause the changing of target shapes were analyzed and classic target shapes presented on obtained infrared images were concluded.Secondly,the classic shapes were used as templates while establishing the new neighborhood system of MRF.Thirdly,to achieve the optimal detection performance,a criterion function for adaptively selecting the proper neighborhood for each pixel was proposed and at last a new potential function using finite difference operator was proposed for the classification of target and background at each pixel.For the usage of adaptive neighborhood system,the proposed algorithm has following advantages:further reduction of the threshold crossing rate of target detection in single image frame while maintaining the target detection rate and better preservation of target shape details than algorithms using classic neighborhood system of MRF.By simulations and experiments,the results show that the proposed algorithm can optimally detect targets under various image Signal-to-clutter ratios,and perfectly protect target shape details.更多还原