Correlation-based multi-shape granulometry with application in porous silicon nanomaterial characterization
- 256 Downloads
Image-based granulometry measures the size distribution of objects in an image of granular material. Usually, algorithms based on mathematical morphology or edge detection are used for this task. We propose an entirely new approach, using cross correlations with kernels of different shapes and sizes. We use pyramidal structure to accelerate the multi-scale searching. The local maxima of cross correlations are the primary candidates for the centers of the objects. These candidate objects are filtered using criteria based on their correlations and intersection areas with other objects. Our technique spatially localizes each object with its shape, size and rotation angle. This allows us to measure many different statistics (besides the traditional objects size distribution) e.g. the shape and spatial distribution of the objects. Experiments show that the new algorithm is greatly robust to noise and can detect even very faint and noisy objects. We use the new algorithm to extract quantitative structural characteristics of Scanning Electron Microscopy (SEM) images of porous silicon layer. The new algorithm computes the size, shape and spatial distribution of the pores. We relate these quantitative results to the fabrication process and discuss the rectangle porous silicon formation mechanism. The new algorithm is a reliable tool for the SEM image processing.
KeywordsPorous silicon Granulometry
The authors thank to CNPq for the financial support.
- 3.G. Matheron, Random Sets and Integral Equation (Wiley, New York, 1978)Google Scholar
- 6.L. Vincent, Fast Grayscale Granulometry Algorithms. In Proceedings of the International Symposium on Mathematical Morphology, Fontainebleau (1994)Google Scholar
- 7.Mathworks Image Processing Toolbox 6.4 demo “Granulometry of Snowflakes”, accessed on 2012. http://www.mathworks.com/products/image/demos.html?file=/products/demos/shipping/images/ipexsnow.html
- 8.N.H. Maerz, T.C. Palangio, J.A. Franklin, WipFrag image based granulometry system. In Proceedings of the FRAGBLAST, 5 Workshop on Measurement of Blast Fragmentation, Montreal, pp. 91–99 (1996)Google Scholar
- 9.R.C. Gonzalez, R.E. Woods, Digital Image Processing, 2nd edn. (Prentice-Hall, New Jersey, 2002)Google Scholar
- 10.J.P. Lewis, Fast normalized cross-correlation. Vision Interface, pp. 120–123 (1995)Google Scholar
- 11.S.A. Araújo, H.Y. Kim, Ciratefi: An RST-invariant template matching with extension to color images. Integr. Comput. Aided Eng. 18(1), 75–90 (2011)Google Scholar
- 16.G. Takacs, V. Chandrasekhar, S. Tsai, D. Chen, R. Grzeszczuk, B. Girod, Unified real-time tracking and recognition with rotation-invariant fast features. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition (CVPR), pp. 934–941 (2010)Google Scholar
- 18.D.R. Huanca, J. Ramirez-Fernandez, W.J. Salcedo, Morphological and structural effect of aluminum on macroporous silicon layer. J. Mat. Sci. Eng. 4(8), 55–59 (2010)Google Scholar
- 19.S.K. Ghandi, VLSI Fabrication Principles: Silicon and Gallium Arsenide (Wiley-Intercience Publication, New York, USA, 1983)Google Scholar
- 20.S. Wolf, Silicon Processing for the VLSI Era, vol. II (Lattice Press, California, USA, 1990)Google Scholar