摘要
为了识别退化的交通标志图像,采用模糊-仿射联合不变矩直接提取图像的特征,并针对各阶模糊-仿射联合不变矩数量级差异较大问题,提出一种数量级标准化算法,避免了需要较大计算量的图像复原处理过程。同时在深入研究径向基概率神经网络的基础上,采用全局K-均值算法优化其网络结构,并将其用于交通标志图像的分类识别。仿真结果表明,模糊-仿射联合不变矩是一种有效的处理退化交通标志图像的方法,所设计的径向基概率神经网络分类器不仅具有精简的结构而且有较好分类精度和推广性能。
For the recognition of degraded traffic sign symbols,the combined blur-affine invariants（CBAIs） are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computation.A new magnitude normalization method is proposed for the great differences of magnitude of combined blur-affine invariants.By deeply discussing the radial basis probabilistic neural network（RBPNN）,a novel structure optimization algorithm for RBPNN is proposed using global K-means algorithm,and the classifier was applied to the classification of degraded traffic signs.The simulation results indicate that CBAIs are efficient for the feature extraction of degraded images,and the designed network is not only parsimonious but also has competitive generalization performance.
出处
《计算机仿真》
CSCD
北大核心
2010年第1期281-284,304,共5页
Computer Simulation
关键词
径向基概率神经网络
交通标志
模糊-仿射联合不变矩
Radial basis probabilistic neural networks（RBPNN）
Traffic sign
Combined blur-affine invariants（CBAIs）
