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Depth and Shape estimation from focus in scanning electron microscope for micromanipulation

机译:聚焦和深度扫描电子显微镜显微操作的形状估计

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Inter-object depth estimation is always a major concern for micromanipulation using scanning electron microscope (SEM). So far, various methods have been proposed for estimating this depth based on stereoscopic imaging. Most of them require external hardware unit or manual interaction during the process. In this paper, using the image focus information, different methods are presented for estimating the inter-object depth for micromanipulation and the local pixel point depth for 3D shape reconstruction. In both cases, the normalized variance has been used as the sharpness criteria. For inter-object depth estimation, a visual servoing-based autofocusing method has been used to maximize the sharpness in object region windows. For Shape reconstruction, a stack of images are acquired by varying the working distance. These images are processed to find the maximum sharpness of each pixel and consequently reconstructing the surface. Developments are validated in a robotic handling scenario where the scene contains a microgripper and silicon microstructures.
机译:对象间深度估计始终是使用扫描电子显微镜(SEM)进行显微操作的主要关注点。到目前为止,已经提出了各种方法用于基于立体成像来估计该深度。它们中的大多数在此过程中需要外部硬件单元或手动交互。在本文中,利用图像焦点信息,提出了不同的方法来估计对象之间的深度以进行显微操作,并估计局部像素点深度以进行3D形状重建。在这两种情况下,均使用标准化的方差作为清晰度标准。对于物体间深度估计,已经使用基于视觉伺服的自动聚焦方法来最大化物体区域窗口中的清晰度。对于形状重建,通过改变工作距离来获取一堆图像。对这些图像进行处理,以找到每个像素的最大清晰度,从而重建表面。在场景包含微型夹具和硅微结构的机器人处理场景中对开发进行了验证。

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