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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