一种改进的一维最大熵星图分割算法

在CCD天文测量技术中，星点质心坐标提取的精度直接影响野外天文测量的坐标精度，而星图分割算法是数字星图处理技术中最为关键的一环。一维最大熵星图分割算法具有良好的二值化效果，且能够充分保留星图图像信息，其可靠性与准确性均已得到了试验验证。本文基于一维最大熵星图分割算法，提出了一种改进的星点区域划分算法，可以在不影响精度和可靠性的前提下，大幅提升一维最大熵算法的运算效率，进一步提高野外天文测量作业的效率，在实际作业中，具有更好的可应用性。

An improved one-dimensional maximum entropy star image segmentation algorithm

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.