doi: 10.4304/jsw.6.8.1476-1483
A Novel Intelligent Technique for Recognition of Coke Optical Texture
- 1 School of Computer and Information, Hefei University of Technology, Hefei, China
- 2 School of Electric and Information, Anhui University of Technology, Maanshan, China
- 3 College of Mathematics and Information Engineering, JiaXing University, JiaXing, China
Abstract
The classification and recognition of the coke optical texture is one of the key elements to determine the quality and guide the production of cokes. Since the traditional methods are not so ideal in the spatial and frequency domains, a novel algorithm is proposed in this paper to bridge the gap. Firstly, the coke micrograph is decomposed by a new contourlet packet (CP) for multi-scale and multi-direction, which introduces a nonsubsampled wavelet transform and a nonsubsampled directional filter banks (NSDFB). Furthermore, an adaptively weighted 2- directonal 2-dimension PCA method is put forward to extract the feature, which not only reduce the data dimensions, but also help to obtain a set of optimal basis in decomposed sub-bands. Finally, the classes of optical texture in coke micrograph are identified according to the knowledge-based similarity measure criteria on eigenvectors of selected basis. The experimental results indicate that the proposed scheme has a higher and more stable recognition rate than the conventional methods.
Keywords
- coke
- micrograph;
- optical
- texture;
- contou
How to Cite
Fang Zhou, Guangxue Yue, Jianguo Jiang, and Peizhen Wang, "A Novel Intelligent Technique for Recognition of Coke Optical Texture," Journal of Software, vol. 6, no. 8, pp. 1476-1483, 2011. https://doi.org/ 10.4304/jsw.6.8.1476-1483
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).