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	基于RBF-NN的压印凹凸字符质量检测研究
	曹建海 ; 李龙 ; 路长厚 ; CAO Jian-hai ; LI Long ; LU Chang-hou
	2010-06-09 ; 2010-06-09
关键词	灰度字符 质量检测 径向基函数神经网络(RBF-NN) 投影变换 gray-scale character quality inspecting radial basis function neural network(RBF-NN) projection transform TP274.4
其他题名	Quality Inspecting for the Pressed Protuberant Character Based RBF-NN
中文摘要	提出了在灰度图像上直接提取压印字符的圆周投影和径向投影特征、基于径向基函数神经网络(RBF-NN)的压印凹凸字符质量检测新方法。检测实验表明,在灰度图像上提取检测特征,不仅保留了压印字符的原始特征,增强了抗干扰性,而且摈弃了复杂的字符图像二值化算法,减少了检测用时。该方法的检测速度为240字符/min,正确率为98.77%,满足标牌压印机的在线检测要求。; The pressed protuberant character is a reflectorized character on the difference of reflectance.The characteristic of the character image differs completely with a general character based on the chromatic difference of background and foreground.A new method of quality inspecting for the pressed character based on the radial basis function neural network(RBF-NN) is presented.The method proposes the direct gray-scale feature extraction on the ring and radial projection algorithm.The results show that the method keeps the integrity feature of the protuberant character information dramatically and takes on a higher performance of anti-jamming,and improves the inspecting speed,moreover abandons the complex image binarization algorithm.Its speed is 240 characters per minute,and the accuracy is 98.77%.It satifies the requirements of the quality inspecting on-line system for the metal tag presser.; 山东省重点产业化资助项目(0203c06)
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/55922]
专题	清华大学
推荐引用方式 GB/T 7714	曹建海,李龙,路长厚,等. 基于RBF-NN的压印凹凸字符质量检测研究[J],2010, 2010.
APA	曹建海,李龙,路长厚,CAO Jian-hai,LI Long,&LU Chang-hou.(2010).基于RBF-NN的压印凹凸字符质量检测研究..
MLA	曹建海,et al."基于RBF-NN的压印凹凸字符质量检测研究".(2010).

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