摘要
星体轨迹观测的难点在于所观测的星体一般为低对比度的弱小目标。传统图像处理方法中采用的一维最大熵阈值分割法仅基于原始直方图,对噪声较敏感,对真实目标尤其是弱小目标的提取造成很大影响。本文介绍了一种改进的二维熵阈值快速算法,从数学运算角度采用递推迭代方法去除冗余计算量,并根据图像对比度低,目标模糊,直方图分布集中的特点,将二维直方图的尺寸大大缩小,使得循环次数大大减少,显著提高了计算速度,大大提高了对弱暗模糊的小型星体的实时测量精度,对噪声具有很强的抗干扰能力。
The stars in deep sky are low contrast targets. Traditional one-dimensional maximum entropy thresholding algorithm is vulnerable to the noise, and the calculation of two-dimensional entropy methods is too large and takes too much time. This paper proposes an improved two-dimensional entropy threshold algorithm. We use recursion iteration method to eliminate the redundancy calculation, and reduce the size of two-dimensional histogram based on the deep sky stars characteristic, such as low contrast, fuzziness and the centralized histogram. Our method improves the capability of trailing the ebb and small star, and increases the precision of tracing.
出处
《微计算机信息》
北大核心
2007年第04X期197-198,共2页
Control & Automation
关键词
深空探测
轨迹测量
二维熵
deep sky detection, star trail forecasting, two-dimensional entropy
作者简介
姚志军（1977-），男，汉族，吉林省长春市人，助理研究员．硕士研究生，研究方向为：数字图像处理，实时目标捕获与跟踪．模式识别等。 通讯地址：（130033吉林吉林省长春市东南湖大路16号长春光机所图像室）姚志军
