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作 者:江旻珊[1] 闫瑾 徐晓立 张学典[1] Jiang Minshan;Yan Jin;Xu Xiaoli;Zhang Xuedian(Institute of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
机构地区:[1]上海理工大学光电信息与计算机工程学院,上海200093
出 处:《光电工程》2018年第12期63-71,共9页Opto-Electronic Engineering
基 金:国家重大科学仪器设备开发专项资助课题(2013YQ03065104)~~
摘 要:对焦窗口的选择是实现显微镜自动对焦的关键步骤。针对传统的对焦窗口选取方法不能准确定位目标物体的问题,本文提出了一种改进的人工鱼群对焦窗口法。以整幅图像中细节最丰富的区域作为对焦窗口的选取依据,充分利用人工鱼群算法良好的全局寻优能力,在整幅图像中选取最佳对焦窗口;将全局优值添加到每条人工鱼的行为更新中,使其能快速移动到当前最佳位置甚至是全局最优位置。此外,在算法中引入了淘汰机制,在保证精度的前提下,提高算法的收敛速度;再根据算法中公告板的特点,结合趋势对比法识别干扰区域,有效排除非目标区域的影响。实验表明,该方法得到的对焦窗口,可以更好地对目标物体进行对焦,大大提高了自动对焦的精确度,并且构建对焦窗口的效率较传统方法提高了1.65倍。The selection of the focusing window is the key procedure in achieving precise automatic focus of the microscope. For the traditional focus window selection method, the limitation is that the target object cannot be ac-curately positioned. This paper proposes an improved artificial fish focusing window method. The method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position. Under the premise of ensuring accuracy, the elimination behavior is introduced to improve the convergence speed of the algorithm in the later period. Furthermore, according to the characteristics of the bulletin board in the algorithm, the interference area is identified with the trend comparison method, and the influence of the non-target area is effectively excluded. Experiment results show that the focusing window obtained by this algorithm can be well-suited for the target object, greatly improve the accuracy of autofocus, and make the efficiency improvement 1.65 times than the traditional method.
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