[1]翟传敏,杜吉祥,黄 飞.基于径向基概率神经网络的工程图纸图形符号识别[J].智能系统学报,2006,1(01):88-91.
 ZHAI Chuan-min,DU Ji-xiang,HUANG Fei.Graphic symbol recognition of engineering drawings based on radial basis probabilistic neural networks[J].CAAI Transactions on Intelligent Systems,2006,1(01):88-91.
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基于径向基概率神经网络的工程图纸图形符号识别(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第1卷
期数:
2006年01期
页码:
88-91
栏目:
出版日期:
2006-03-25

文章信息/Info

Title:
Graphic symbol recognition of engineering drawings based on radial basis probabilistic neural networks
文章编号:
1673-4785(2006)01-0088-04
作者:
翟传敏1杜吉祥23黄 飞1
1.合肥学院机械工程系,安徽合肥230022;2.国立华侨大学计算机系,福建泉州362021;3.中国科学技术大学信息科学技术学院,安微合肥230026
Author(s):
ZHAI Chuan-min1DU Ji-xiang23HUANG Fei1
1.Department of Mechanical Engineering, Hefei University, Hef ei 230022,China; 2.Department of Computer,National Huaqi ao University, Quanzhou,362021,China; 3.School of information Science and techno logy, University of Science and Technology of China, Hefei,230026,China
关键词:
径向基概率神经网络图形符号工程图纸识别
Keywords:
radial basis probabilistic neural network graphicsy mbol engineering drawings recognition
分类号:
TP31
文献标志码:
A
摘要:
基于径向基概率神经网络,提出一种扫描工程图纸图像分割后的图形符号识别方法.针对已分割的扫描工程图纸图形符号图像,首先进行二值化处理,然后对二值图形符号图像进行Hu不变矩特征提取,再使用一种新型的径向基概率神经网络进行分类,从而实现图像识别.为加快径向基概率神经网络的收敛速度,采用递归最小二乘算法进行训练.实验结果表明,径向基概率神经网络在识别性能与速度等方面非常适合于工程图纸的图形符号识别.
Abstract:
A novel graphic symbol recognition approach of engineering drawings based on radial basis probabilistic neural network s (RBPNN) is proposed. The Hu invariant moment method is applied to extract the shape features of the segmented graphic symbol image of scanned engineering draw ings. The experimental results show that the RBPNN achieves a higher recognition rate and better classification efficiency with respect to radial basis function neural networks (RBFNN) and multi-layer perceptron networks (MLPN) for the gra phic symbol recognition task.

参考文献/References:

[1]董玉德,赵    韩,王    平,等.工程图纸识别与理解的研究现状分析[J]. 合肥工业大学学报(自然科学版), 2005, 28(1):29-33.
DONG Yude, ZHAO Han, WANG Ping, et al. Analysis of research status of enginee rin g drawings recognition and interpretation [J]. Journal of Hefei University of Technology, 2005, 28(1):29-33.
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HUANG Deshuang. Systematic theory of neural networks for pattern recognitio n [M]. Beijing: Publishing House of Electronic Industry of China, 1996.
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备注/Memo

备注/Memo:
收稿日期:2006-02-15.
基金项目:国家自然科学基金资助项目(60405002);合肥学院自然科学研究基金资助项目(05ky013zr).
作者简介:
翟传敏,女,1977年生,讲师,2002年在合肥工业大学获工学硕士学位.主要从事模式识别、机械CAD相关研究工作.
杜吉祥,男,1977年生,博士后,2005年在中国科学技术大学获工学博士学位. 主要研究方向为模式识别与图像处理等.已在国际会议和国际杂志上发表论文10余篇,大部分被SCI、EI收录.
黄    飞,男,1978年生,讲师,2002年在合肥工业大学获工学硕士学位.主要从事CAD /CAM、测控技术及仪器科学技术的研究.
更新日期/Last Update: 2009-04-07