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Micron. 2016 Aug;87:33-45. doi: 10.1016/j.micron.2016.05.004. Epub 2016 May 7.

3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction.

Author information

1
Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA. Electronic address: pahlava2@uwm.edu.
2
Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
3
Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
4
Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA; Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA; Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA.
5
Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA. Electronic address: yuz@uwm.edu.

Abstract

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, such as biological, mechanical, and materials sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around for decades to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and facts about their three-dimensional (3D) structures. 3D surface reconstruction from SEM images leads to remarkable understanding of microscopic surfaces, allowing informative and qualitative visualization of the samples being investigated. In this contribution, we integrate several computational technologies including machine learning, contrario methodology, and epipolar geometry to design and develop a novel and efficient method called 3DSEM++ for multi-view 3D SEM surface reconstruction in an adaptive and intelligent fashion. The experiments which have been performed on real and synthetic data assert the approach is able to reach a significant precision to both SEM extrinsic calibration and its 3D surface modeling.

KEYWORDS:

3D SEM surface reconstruction; 3D microscopy vision; Scanning electron microscope (SEM)

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