One-dimensional Maximum Entropy Image Segmentation Algorithm Based on the Small Field of View of Measuring Robot Star Map

doi:10.11947/j.AGCS.2018.20170202

Abstract

Abstract: As one of the fundamental problems in processing star map,image segmentation plays a significant part in ensuring precise field astronomical survey.Image binarization is the key procedure in the image segmentation,but it is extremely difficult to extract star targets from complex sky background using conventional threshold segmentation algorithms.Considering that the Leica video measurement robot TS50i shows features such as the small field of view,single star point,weak target,and single peak,one-dimensional maximum entropy method is firstly proposed to split the star maps.The proposed algorithm is verified by comparison with conventional threshold segmentation algorithms.It is indicated that the one-dimensional maximum entropy algorithm can achieve satisfied binarization processing results while adequately preserve the image information at the same time.Simulation experiments using real star maps show that the extraction method based on this algorithm is accurate and reliable with an accuracy of an order of magnitude better than requirements of the field first-class astronomical survey,hence it can satisfy the need of precise field astronomical survey.

Key words: CCD astronomical measurement, image binaryzation, threshold segmentation, one-dimensional maximum entropy

CLC Number:

P222

SHI Chunlin, ZHANG Chao, CHEN Changyuan, DU Lan, YE Kai, HAN Zhong. One-dimensional Maximum Entropy Image Segmentation Algorithm Based on the Small Field of View of Measuring Robot Star Map[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(4): 446-454.

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