摘要
针对金属零件上二维条码光照分布不均、点扩散、对比度低与污染干扰等问题,提出一种基于原灰度图像小区域相邻模块对比提取二维条码数据的算法.首先通过峰度值排序法及模块区域微调法由粗到精定位每个二维条码模块位置,然后基于原灰度图像利用遗传算法提取二维条码的数据信息,得到最终的提取结果.与传统二维条码数据提取算法的实验结果证明,该算法对于复杂金属背景上的二维条码识读具有更高的可靠性.
This paper describes a 2D bar code extraction approach that is capable of processing pictures of 2D bar codes on metal parts with uneven distribution of light intensity,point spread,low contrast and pollution interference.The algorithm uses kurtosis value-sorting and module area fine tuning to locate each module＇s position which is detected from coarse-level to fine-level,then the final extraction data of 2D bar code is obtained by a genetic algorithm based on the original gray-scale image.Compared with traditional methods,the proposed algorithm has higher reliability for 2D bar code extraction on complex metal parts.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2012年第5期612-619,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家"八六三"高技术研究发展计划(2007AA040701-3)
国防基础科研项目(A2720110011)
关键词
二维条码
金属背景
抗干扰识别
遗传算法
two-dimension barcode
metal background
interference-free identification
genetic algorithm
