Tafti Ahmad P, Holz Jessica D, Baghaie Ahmadreza, Owen Heather A, He Max M, Yu Zeyun
Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
Micron. 2016 Aug;87:33-45. doi: 10.1016/j.micron.2016.05.004. Epub 2016 May 7.
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.
微观物体的结构分析在多个科学学科中都是一个长期存在的课题，比如生物学、机械学和材料科学。扫描电子显微镜（SEM）作为一种很有前景的成像设备已经存在了几十年，它通过实现大于一纳米的放大倍数、对比度和分辨率来确定样本的表面特性（例如成分或几何形状）。虽然扫描电子显微镜图像仍然是二维（2D）的，但许多研究和教育问题确实需要有关其三维（3D）结构的知识和事实。从扫描电子显微镜图像进行三维表面重建能够显著增进对微观表面的理解，从而对所研究的样本进行信息丰富且定性的可视化。在本论文中，我们整合了包括机器学习、反差方法和极线几何等多种计算技术，以自适应和智能的方式设计并开发了一种名为3DSEM++的新颖高效方法，用于多视图三维扫描电子显微镜表面重建。在真实数据和合成数据上进行的实验表明，该方法在扫描电子显微镜外部校准及其三维表面建模方面都能够达到很高的精度。
