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Automated 3D Modeling and Analysis of Metallic Materials Using Multiple LC-SEM Images
 

Summary: Automated 3D Modeling and Analysis of Metallic Materials Using Multiple
LC-SEM Images
W. Hao,* D.L. Page,* B.R. Abidi,* M.A. Abidi*, J. Frafjord** and S. Dekanich**
*Department of ECE, University of Tennessee (UT), 1508 Middle Drive, Knoxville, TN 37996
**Y12 National Security Complex, Bear Creek Road, Oak Ridge, TN 37831
The scanning electron microscope (SEM) has been successfully used as an analysis tool for
micro scale materials in the last few decades [1]. Recently, the large chamber (LC) SEM at Y-12
has demonstrated useful results in the analysis of large specimens at nano scales. This paper
summarizes the research efforts conducted by UT and Y-12 in applying state of art 3D computer
vision techniques to LC-SEM imaging, particularly 3D reconstruction and modeling [2]. In such
a way, two or more LC-SEM images taken under appropriate condition (for example, with small
changes in tilt angle), combined with knowledge of the imaging process, can be used to infer the
3D structure of the specimen automatically.
The assumption of this research is that the imaging mechanics of the LC-SEM (and SEM in
general) can be modeled by a projective transformation under certain conditions. Based on this
assumption, we have developed a set of algorithms that implement the state of art in 3D
reconstruction techniques. (The pipeline is illustrated in Fig. 1) First, sparse feature points are
extracted automatically and a robust statistical inference method is adopted to estimate the
epipolar geometry inherent in the multiple images. With the knowledge of the epipolar geometry,
the dense matching between different views of the scene is established using a graph cut

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee

 

Collections: Computer Technologies and Information Sciences