Recognition of Topology Feature of Graphene by Image Processing Technique

Mao Fa Wang; Ji Lin Feng; Xiao Ping Zou; Xiao Li Li; Pan  Li; Qing Jie  Liu; Wei Feng  Shan; Ying Han; Yan Ping  Shen

doi:10.4028/www.scientific.net/AMR.756-759.4133

Paper Titles

Microscopic Image Segmentation of Chinese Herbal Medicine Based on Region Growing Algorithm
p.4110

Dynamic Building Tracking from UAVs Based on Image Manifold Learning
p.4121

Image Denoising Method Based on ^v-Support Vector Regression and Noise Detection
p.4126

Recognition of Topology Feature of Graphene by Image Processing Technique
p.4133

Real-Time 3D Hand Gesture Detection from Depth Images
p.4138

The Passive Location of Emitter Based on Fuzzy Method
p.4143

The SPWM Pulse Width Signal Spectrum Analysis Based on MATLAB
p.4147

The FPGA Implementation of Pulse Wave Feature Extraction
p.4155

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Recognition of Topology Feature of Graphene by...

Recognition of Topology Feature of Graphene by Image Processing Technique

Abstract:

In order to extract characteristic values of graphene, a method based on a series of digital image processing algorithms was proposed in this paper. Using the method, we can automatically analyze topology structure and calculate amount of carbon atoms, and even calculate chiral vector indices (n, m) during in-situ TEM scanning of graphene. At last, we also write the correlative software with c++, which applying the digital image processing algorithms, and give out the processing result of several classic TEM image of graphene.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

4133-4137

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.4133

Citation:

Cite this paper

Online since:

September 2013

Authors:

Mao Fa Wang, Ji Lin Feng, Xiao Ping Zou, Xiao Li Li, Pan Li, Qing Jie Liu, Wei Feng Shan, Ying Han, Yan Ping Shen

Keywords:

Digital Image Processing, Feature Values, Graphene (GR), Topology Structure

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Florian Banhart, Jani Kotakoski, and Arkady V. Krasheninnikov, Structural Defects in Graphene, ascNano, vol. 5(1), pp.26-41, (2011).

Google Scholar

[2] Jannik C. Meyer, C. Kisielowski, R. Erni, Marta D. Rossell, M. F. Crommie, and A. Zettl, Direct Imaging of Lattice Atoms and Topological Defects in Graphene Membranes, NANO LETTERS. vol. 8(11) pp.3582-3586, (2008).

DOI: 10.1021/nl801386m

Google Scholar

[3] Wang, M. F, Zou,X. P, Recognition of atomically-resolved STM images of graphite, , Journal of Computational and Theoretical NanoScience. vol. 7 pp.404-407, (2010).

DOI: 10.1166/jctn.2010.1373

Google Scholar

[4] M. F. Wang, J. L. Feng, J. Y, F. P. Gao, X. P. Zou, Y. H. Yang Recognition of amount of carbon atoms of graphite STM, Journal of Computational and Theoretical NanoScience. vol. 8, pp.2204-2208, (2010).

DOI: 10.1166/jctn.2011.1944

Google Scholar

[5] M. A. Butt and P. Maragos, Optimum design of chamfer distance transforms, IEEE Transactions on Image Processing. vol. 7, pp.1477-1484, (1998).

DOI: 10.1109/83.718487

Google Scholar

[6] Distance transform, Wikipedia internet web, the free encyclopedia. last modified on 21 December (2010).

Google Scholar

[7] Z. Lu, Image processing programming with c/c++, Tsinghua university press, Beijing, (2006).

Google Scholar