Abstract
The Scanning Electron Microscope (SEM) as 2D imaging equipment has been widely used in biology and material sciences to determine the surface attributes of a microscopic object. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which allow for quantitative measurements and informative visualization of the systems being investigated. In this contribution, we present a Differential Evolutionary (DE) approach for both SEM extrinsic calibration and 3D surface reconstruction. We show that the SEM extrinsic calibration and its 3D shape model can be accurately estimated in a global optimization platform. Several experiments from various perspectives are performed on real and synthetic data to validate the speed, reliability and accuracy of the proposed system. The present work is expected to stimulate more interest and draw attentions from the computer vision community to the fast-growing SEM application area.
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References
Meshlab (2005), http://meshlab.sourceforge.net/
Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S.M., Szeliski, R.: Building rome in a day. Commun. ACM 54(10), 105–112 (2011)
Albouy, B., Treuillet, S., Lucas, Y., Birov, D.: Fundamental matrix estimation revisited through a global 3d reconstruction framework. In: Advanced Concepts for Intelligent Vision Systems (2004)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Surf: Speed up robust features. Computer Cision and Image Understanding (CVIU) 110, 346–359 (2008)
Cazaux, J.: Recent developments and new strategies in scanning electron microscopy. Journal of Microscopy 217, 16–35 (2005)
Chakraborty, U.K.: Advances in Differential Evolution. Prentice-Hall, USA (2008)
Chen, D., Miyamoto, A., Kaneko, S.: Robust surface reconstruction in sem with two bse detectors. Mecatronics REM 2012 (2012)
Cignoni, P., Rocchini, C., Scopigno, R.: Metro: Measuring error on simplified surfaces. Computer Graphics Forum 17, 167–174 (1998)
Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.: Discrete-continuous optimization for large-scale structure from motion. In: CVPR (2011)
Egerton, R.: Physical Principles of Electron Microscopy: An Introduction to TEM, SEM, and AEM. Springer, USA (2005)
Feoktistov, V.: Differential Evolution. Springer, Germany (2006)
Fischler, M.A., Bolles, R.C.: Random sample consesus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM (1981)
Ghosh, A., Mondal, A., Ghosh, S.: Moving object detection using markov random field and distributed differential evolution. Applied Soft Computing 15, 121–136 (2014)
Hartely, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, UK (2004)
Kodama, T., Li, X., Nakahira, K., Ito, D.: Evolutionary computation applied to the reconstruction of 3-d surface topography in the sem. Journal of Electron Microscopy 54(5), 429–435 (2005)
Lourakis, M.A., Argyros, A.: Sba: A software package for generic sparse bundle adjustment. ACM Trans. Math. Software 36(1), 1–30 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Munkres, James: Topologyn. Prentice Hall, USA (1999)
Nyirarugira, C., Taeyong, K.: Adaptive differential evolution algorithm for real time object tracking. IEEE Transaction on Consumer Electronics 59 (2013)
Paluszynski, J., Slowko, W.: Surface reconstruction with the photometric method in sem. Vaccum 78, 533–537 (2005)
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3d face model for pose and illumination invariant face recognition. In: Proceedings of the 6th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS) for Security, Safety and Monitoring in Smart Environments (2009)
Pintus, R., Podda, S., Vanzi, M.: An automatic alignment procedure for a 4-source photometric stereo technique applied to scanning electron microscopy. In: IMTC- Instrumentation and Measurement (2006)
Samak, D., Fischer, A., Rittel, D.: 3d reconstruction and visualization of microstructure surfaces from 2d images. Annals of the CIRP 56 (2007)
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment – A modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000)
Wohler, C.: 3D computer vision efficient methods and applications. Springer, Germany (2013)
Zolotukhin, A., Safonov, I., Kryzhanovskii, K.: 3d reconstruction for a scanning electron microscope. Pattern Recognition and Image Analysis 23(1), 168–174 (2013)
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Tafti, A.P., Kirkpatrick, A.B., Owen, H.A., Yu, Z. (2014). 3D Microscopy Vision Using Multiple View Geometry and Differential Evolutionary Approaches. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_14
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DOI: https://doi.org/10.1007/978-3-319-14364-4_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
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