环状编码标记点的检测与识别

宋丽梅; 陈昌曼; 陈卓; 覃名翠; 李大鹏

doi:10.3788/OPE.20132112.3239

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环状编码标记点的检测与识别

信息科学 | 更新时间：2020-08-12

- 环状编码标记点的检测与识别
- Detection and recognition of cyclic coded targets
- 光学精密工程 2013年21卷第12期页码：3239-3247
- 作者机构：
  
  天津工业大学电工电能新技术天津市重点实验室天津,300387
- 作者简介：
- 基金信息：
  
  单目高精度大型物体彩色三维数字化测量原理研究();基于视觉的织物疵点三维检测和三维识别原理研究();双目SFS彩色三维织物疵点在线检测原理研究()
- DOI：10.3788/OPE.20132112.3239
  中图分类号： TP391
- 收稿日期：2013-05-23，
  
  修回日期：2013-06-27，
  
  网络出版日期：2013-12-25，
  
  纸质出版日期：2013-12-25
- 稿件说明：
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宋丽梅, 陈昌曼, 陈卓, 覃名翠, 李大鹏. 环状编码标记点的检测与识别[J]. 光学精密工程, 2013,21(12): 3239-3247

SONG Li-Mei, CHEN Chang-Man, CHEN Zhuo, QIN Ming-Cui, LI Da-Peng. Detection and recognition of cyclic coded targets[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3239-3247
宋丽梅, 陈昌曼, 陈卓, 覃名翠, 李大鹏. 环状编码标记点的检测与识别[J]. 光学精密工程, 2013,21(12): 3239-3247 DOI： 10.3788/OPE.20132112.3239.

SONG Li-Mei, CHEN Chang-Man, CHEN Zhuo, QIN Ming-Cui, LI Da-Peng. Detection and recognition of cyclic coded targets[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3239-3247 DOI： 10.3788/OPE.20132112.3239.

摘要

针对近景摄影测量中对编码标记点进行精确定位并对具有唯一编码值的编码标记点进行解码识别的要求，提出了一种环状编码标记点自动检测和识别算法。该算法首先对图像进行canny边缘检测，通过一系列限制条件和计算封闭轮廓质心来过滤噪声和非编码标记点。然后，采用最小二乘椭圆拟合进行编码标记点定位，结合椭圆拟合误差判断分割编码标记点轮廓并进行填充。最后，提出一种将局部同心椭圆变换为平行直线的ALPC (Affine LOG Polar Coordinate)变换，对分割出的编码标记点进行ALPC变换，利用变换后的图像特征进行解码。实验结果表明，利用本文算法对于编码标记点的定位可以达到亚像素级别；且当相机光轴与编码标记点法线夹角小于60时平均识别准确率能达到98.8%，当夹角达到70时识别准确率仍然能达到90.2%；在复杂背景下的识别准确率能够达到96.46%。

Abstract

On the requests of coded targets in the close-range photogrammetry for precision position and decode recognition

an algorithm to automatically detect and recognize the cyclic coded targets was proposed. Firstly

the canny edge detection was used to process images

not only the noises but also the non-coded target were filtered by means of calculating the centroids of closed contours and a series of criteria. Then the least square ellipse fitting was taken to locate the coded target

and each coded target contour could be segmented and filled when the fitting error was satisfied. Finally

a transformation method named Affine LOG Polar Coordinate (ALPC)that has the property of transforming local concentric ellipses into parallel straight lines was proposed to transform each coded target for decoding. Experimental results show that the algorithm presented in this paper can locate the coded targets to a sub-pixel level

and the average recognition accuracy rate can reach 98.8% when the angle between the camera optical axis and the normal of coded target is less than 60 . Furthermore

the recognition accuracy rate can still reach 90.2% when the angle is 70

and the recognition accuracy rate can reach 96.46% under the complex background. In conclusion

the algorithm proposed can fully satisfy the precision and accuracy requirements of coded target location and recognition in the close-range photogrammetry.

关键词

Keywords

references

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