An improved one-dimensional maximum entropy star image segmentation algorithm

doi:10.13474/j.cnki.11-2246.2020.0318

Bulletin of Surveying and Mapping ›› 2020, Vol. 0 ›› Issue (10): 53-57.doi: 10.13474/j.cnki.11-2246.2020.0318

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An improved one-dimensional maximum entropy star image segmentation algorithm

ZHANG Geng¹, ZHANG Chao¹, MI Kefeng^1,2, ZHAN Yinhu¹, LI Chonghui¹

1. Information Engineering University, Zhengzhou 450001, China;
2. Troop 61363, Xi'an 710054, China

Received:2019-12-02 Online:2020-10-25 Published:2020-10-29

Abstract

Abstract: In CCD astronomical measurement technology, the accuracy of star point centroid coordinate extraction directly affects the coordinate precision of field astronomical measurement, and star image segmentation algorithm is the most critical part of digital star image processing technology. The one-dimensional maximum entropy star image segmentation algorithm has a very good binarization effect, and can fully retain the information of star image, and its reliability and accuracy have been experimentally verified. Based on the one-dimensional maximum entropy star image segmentation algorithm, this paper proposes an improved star point region partitioning algorithm, which can greatly improve the computational efficiency of the one-dimensional maximum entropy algorithm without reducing the accuracy and reliability, and improve the efficiency of field astronomical measurement operations. The availability of astronomical measurement operations has better applicability in practical operations.

Key words: one-dimensional maximum entropy, division of the star point region, CCD astronomical measurement, automatic astronomical measurement

CLC Number:

ZHANG Geng, ZHANG Chao, MI Kefeng, ZHAN Yinhu, LI Chonghui. An improved one-dimensional maximum entropy star image segmentation algorithm[J]. Bulletin of Surveying and Mapping, 2020, 0(10): 53-57.

References

[1] 原玉磊. 鱼眼相机恒星法检校技术研究[D]. 郑州:信息工程大学, 2012.
[2] 时春霖. 基于视频测量机器人的自动天文测量技术研究[D]. 郑州:信息工程大学, 2018.
[3] 时春霖,张超,陈长远,等. 测量机器人小视场星图一维最大熵星点图像分割算法[J]. 测绘学报, 2018, 47(4):446-454.
[4] KAPUR J N,SAHOO P K,WONG A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision,Graphics,and Image Processing, 1985, 29(3):273-285.
[5] 时春霖,张超,袁晓波,等. 天文大地测量的发展现状和展望[J]. 测绘工程, 2019, 28(2):33-40.
[6] 郭敏,张红英. CCD数字摄影在天文定位测量中的应用探讨[J]. 测绘技术装备, 2005, 7(1):28-29.
[7] 张超. 基于电子经纬仪的天文测量系统及应用研究[D]. 郑州:信息工程大学, 2009.
[8] 宋飞杰,张超,王若璞,等. Leica-TS30在天文测量中的应用[J]. 测绘科学技术学报, 2015, 32(2):135-139.
[9] 陈冬岚,刘京南,余玲玲. 几种图像分割阈值选取方法的比较与研究[J]. 机械制造与自动化, 2003(1):77-80.
[10] 程毛林. 数据拟合函数的加权最小二乘积分法[J]. 数学的实践与认识, 2012, 42(4):70-76.
[11] 侯格贤,毕笃彦,吴成柯. 图象分割质量评价方法研究[J]. 中国图象图形学报, 2000, 5(1):42-46.
[12] 章毓晋. 图象分割评价技术分类和比较[J]. 中国图象图形学报, 1996, 1(2):151-158.
[13] 刘春燕. 图像分割评价方法研究[D]. 西安:西安电子科技大学, 2011.
[14] 王润生. 图像理解[M]. 长沙:国防科技大学出版社, 1995:113-117.
[15] MAHMOUDI L. A survey of entropy image thresholding techniques[C]//Proceedings of the 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). Beirut:[s.n.], 2012:204-209.
[16] LEVINE M D,NAZIF A M. Dynamic measurement of computer generated image segmentations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, 7(2):155-164.

An improved one-dimensional maximum entropy star image segmentation algorithm

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