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首页> 外文会议>Conference on Optical Design and Testing; 20071112-15; Beijing(CN) >Microscope Auto-focusing System with the Self-adaptive Mountain-climbing Search Method based on PC Control
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Microscope Auto-focusing System with the Self-adaptive Mountain-climbing Search Method based on PC Control

机译：基于PC控制的自适应爬山搜索方法的显微镜自动聚焦系统

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摘要

A microscope auto-focusing system using the self-adaptive mountain-climbing search (SAMCS) method is introduced based on personal computer (PC) control. It mainly consists of four parts: the microscope, the digital camera to get the video images, the mechanical part of step motor and the computer to control the focusing process. The precision of the auto-focusing system is to some extent improved through high-resolution color images acquired by the digital camera as well as high subdivision of the step motor drive. An improved searching method - the SAMCS method is presented here. It can effectively improve the searching efficiency while guaranteeing the precision of the auto-focusing system. Based on the normal mountain-climbing search (MCS) algorithm, the SAMCS method takes full consideration of omnidistance concept and local extreme point influences. Thereby it can adaptively adjust the searching range according to different environmental conditions, and has quite good robustness. This feature mainly has two advantages. First, this method is much more exact than the normal mountain-climbing, which can not avoid local fluctuations. Second, it is much faster than the method of only using omnidistance searching to avoid local fluctuations. At the same time, we also take evaluation function and region selection into consideration to reach a faster and more accurate focusing result. And the experimental result demonstrates a good efficiency and accuracy.
机译：介绍了一种基于自适应爬山搜索（SAMCS）方法的显微镜自动调焦系统。它主要由四个部分组成：显微镜，获取视频图像的数码相机，步进电机的机械部分以及控制聚焦过程的计算机。通过数字相机获取的高分辨率彩色图像以及步进电机驱动器的细分，可以在某种程度上提高自动聚焦系统的精度。这里提出一种改进的搜索方法-SAMCS方法。在保证自动聚焦系统精度的同时，可以有效提高搜索效率。 SAMCS方法基于常规的爬山搜索（MCS）算法，充分考虑了全距离概念和局部极点影响。从而可以根据不同的环境条件自适应地调整搜索范围，并具有很好的鲁棒性。此功能主要有两个优点。首先，该方法比无法避免局部波动的正常爬山更为精确。其次，它比仅使用全距离搜索来避免局部波动的方法要快得多。同时，我们还考虑了评估功能和区域选择，以实现更快，更准确的聚焦结果。实验结果表明，该方法具有较高的效率和准确性。

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