摘要
为提高目标识别率,在目标图像融合过程中引入Markov随机场建立类别的先验分布模型,针对模型中参量β的选取问题,提出了基于各类各向异性的期望最大化-最大后验概率-多层次马尔可夫随机场集中式与分布式两种图像融合算法.实验证明,两种融合算法都既可以提高分类准确度,又能够增大抗噪能力,且二者又有不同的特色,可以根据实际要求(如,运算速度、分类准确度、计算负荷等)进行应用选择,用以提高对特定目标进行自动检测与识别的准确性.
In order to improve the target identification,Markov random field（MRF）is introduced in the target fusion process to build prior probability model of a class.Then aiming at selecting model parameter β,an EM-MAP-HMRF feature-level fusion algorithm is proposed based on non-homogeneous class and direction.HMRF is divided into centric and distributed-based fusion schemes.The simulations show that the two new fusion algorithms can improve the classification accuracy,and enhance the ability to anti-interference.However,they have different advantages.The two new schemes can be used in various fusion systems for different applications and improve the effectiveness of detection and identification for specific targets.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2010年第7期1289-1296,共8页
Acta Photonica Sinica
基金
国家教育部博士点基金(20040699015)
西北工业大学青年科技创新基金(5210102-0800-M016206)资助
