计算机应用 ›› 2012, Vol. 32 ›› Issue (04): 1137-1140.DOI: 10.3724/SP.J.1087.2012.01137

• 图形图像技术 • 上一篇    下一篇

基于突变信号检测的光学标记识别图像分割方法

马磊1,2,刘江1,2,李晓鹏1,2,陈霞1,2   

  1. 1. 山东山大鸥玛软件有限公司,济南 250101
    2. 山东省图像采集与处理工程技术研究中心,济南 250101
  • 收稿日期:2011-09-16 修回日期:2011-11-23 发布日期:2012-04-20 出版日期:2012-04-01
  • 通讯作者: 刘江
  • 作者简介:马磊(1960-),男,山东济南人,研究员,博士,主要研究方向:数字图像识别、高速数据采集;
    刘江(1979-),男,山东济南人,工程师,硕士,主要研究方向:文档图像处理算法、海量数据存储;
    李晓鹏(1982-),男,山东济南人,主要研究方向:海量数据存储与检索;
    陈霞(1984-),女,山东泰安人,硕士,主要研究方向:基于内容的图像检索、数字水印。
  • 基金资助:
    山东省信息产业专项基金-大规模数据采集与处理系统

OMR image segmentation based on mutation signal detection

MA Lei1,2,LIU Jiang1,2,LI Xiao-peng1,2,CHEN Xia1,2   

  1. 1. Shandong Engineering Research Institute for Image Acquisition and Processing, Jinan Shandong 250101,China
    2. Shandong Shanda Oumasoft Company Limited, Jinan Shandong 250101,China
  • Received:2011-09-16 Revised:2011-11-23 Online:2012-04-20 Published:2012-04-01
  • Contact: LIU Jiang

摘要: 针对无定位信息的光学标记识别(OMR)图像填涂区的精确定位问题,提出了一种基于小波变换突变信号检测的图像分割方法。该算法首先计算图像的水平和垂直投影函数,然后投影函数经过迭代小波变换后检测其突变点,突变点能够精确地反映OMR信息的边界位置。检测算法的适应性基于有限次数的小波变换和突变信号检测过程。实验结果表明该算法具有较高的分割精度和稳定性,分割精度均方差可以达到0.4167个像素。而且由于算法只使用图像的水平和垂直投影信息,因此具有较高的执行效率;投影函数的统计特性和小波变换的多分辨特性则使得该分割算法对噪声不敏感。

关键词: 光学标记识别, 小波变换, 图像分割, 突变点检测, 多分辨分析

Abstract: Concerning the accurate positioning of Optical Mark Recognition (OMR) images without any position information, an image segmentation approach of mutation signal detection based on wavelet transformation was proposed. Firstly, the horizontal and vertical projective operations were processed, and then these functions were transformed by wavelet to detect mutation points, which can better reflect the boundary of OMR information. This algorithms adaptability is based on limited times of wavelet transform and mutation signal detection. The experimental results demonstrate that the method possesses high accuracy of segmentation and stability, and the mean square error of segmentation accuracy can be 0.4167 pixels. The processing of this method is efficient because the segmentation only used the horizontal and vertical information. This algorithm is not sensitive to noise because of the statistic characteristic of projection functions and multi-resolution characteristic of wavelet tranformation.

Key words: Optical Mark Recognition (OMR), wavelet transformation, image segmentation, singularity detection, multiresolutlon analysis