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American Journal of Computing Research Repository. 2014, 2(4), 61-65
DOI: 10.12691/ajcrr-2-4-2
Open AccessArticle

Computer Vision for Estimating Cooper Density by Optical Microscope Images

Majid Memarian Sorkhabi1,

1Instructor, University Collage of Roshdiyeh, Tabriz, Iran

Pub. Date: December 14, 2014

Cite this paper:
Majid Memarian Sorkhabi. Computer Vision for Estimating Cooper Density by Optical Microscope Images. American Journal of Computing Research Repository. 2014; 2(4):61-65. doi: 10.12691/ajcrr-2-4-2

Abstract

Computer vision is one of the innovative methods in structure analyzing. In this paper computer vision and digital image processing were used for predict area of copper particle in the mineral soil. Iran is one of the most important mineral producers in the word and ranked among 15 major mineral-rich countries. The overall purpose has been to develop skills and techniques on detection and quantification of mineralogy and microstructures by means of various optical microscopy techniques and to study important microstructure parameters. In this research three new techniques are introduced: in first technique: images were decomposed to red, green and blue images. Secondly continuous and discrete wavelet transform of soil images were calculated. Finally percentage of energy for each wavelet coefficient was calculated. Accuracy of this analyze is under ±7.60% error in copper area estimation. symlets is selected the best wavelet for mineral analyzing. The threshold of copper energy coefficient is calculated equal to 3.23x10-4 in optical microscope images.

Keywords:
image processing RGB image wavelet transform symlet wave coefficient energy

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References:

[1]  Mining in Iran: www.en.wikipedia.org/Mining_in_Iran
 
[2]  Jurrus, E., Paiva, A., Watanabe, S., Anderson, J., Jones, B., et al. “Detection of neuron membranes in electron microscopy images using serial neural network architecture,” Med Image Anal, 14, 770-83, 2010.
 
[3]  Torch, S., Usson, Y., Saxod, R., “Automated morphometric study of human peripheral nerves by image analysis”, Pathol Res Pract 185, 567-71, 2010.
 
[4]  Zhao, X., Pan, Z., Wu, J., Zhou, G., Zeng, Y., “Automatic identification and morphometry of optic nerve fibers in electron microscopy images”, Comput Med Imaging Graph, 34, 179-84, 2010.
 
[5]  MATLAB software help: www.mathworks.com
 
[6]  Gonzalez, R.C., Woods, R.E., Digital Image Processing, Prentice Hall, 2007.
 
[7]  Haddad, S. C., Worden, R. H., Prior, D. J., and Smalley, P. C., Quartz cement in the Fontainebleau Sandstone, Paris Basin, France; crystallography and implications for mechanisms of cement growth: Journal of Sedimentary Research, v. 76, p. 244-256, 2006.
 
[8]  Grelk, B., Review of literature and TEAM results, TEAM-project WP2, p. 40, 2005.