%0 Journal Article
%T Identification of Degraded Traffic Sign Symbols Using PNN<br>基于PNN的退化交通标志图像的识别算法研究
%A Li Lun-bo
%A Ma Guang-fu
%A <br>李伦波
%A 马广富
%J 电子与信息学报
%D 2008
%I 
%X A novel feature extraction algorithm is presented for the recognition of traffic sign symbols undergoing degradations in this paper. In order to cope with the degradations, 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 computations. A new magnitude normalization method is proposed for the great differences of magnitude of combined blur-affine invariants. Under the deep discussion of PNN and K-means algorithm, a probabilistic neural network classifier is designed using global K-means algorithm and 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 better generalization performance.
%K 模式识别
%K 概率神经网络
%K 交通标志
%K 模糊.仿射联合不变矩
%K 全局K-均值算法
%K 退化
%K 交通标志
%K 图像复原
%K 分类识别
%K 算法研究
%K Symbols
%K Sign
%K Traffic
%K 推广性能
%K 结构
%K 精简
%K 优化设计
%K 方法
%K 仿真结果
%K 网络分类器
%K 神经
%K 概率
%K 均值算法
%K 聚类算法
%K 分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=E0070440310BBA9A1B633DCCD573C52F&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=DF92D298D3FF1E6E&sid=10AEA069F4433410&eid=F69F61A42EF5D746&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=5&reference_num=16