Determination of steel quality based on discriminating textural feature selection

Chemical Engineering Science

Volumn 66, Issue 23, 2011, Pages 6264-6271

  Kim, Daeyoun ^a   Liu, J Jay ^b   Han, Chonghun ^a  

^a Seoul National University   (South Korea)

^b Pukyong National University   (South Korea)

Author keywords

Chemical processes; Dynamic simulation; Feature selection; Process modeling; Systems engineering; Wavelet texture analysis

Indexed keywords

BEST BASIS; CHEMICAL PROCESS; CLASSIFICATION ERRORS; COMMON FEATURES; IMAGE DATA; IMAGE DATASETS; INDUSTRIAL STEEL; LOCAL DISCRIMINANT BASIS; PROCESS MODELING; PRODUCT SURFACE; QUALITY DETERMINATION; REAL TIME; SENSOR SYSTEMS; STEEL QUALITY; TEXTURAL CHARACTERISTIC; TEXTURAL FEATURE; TEXTURAL INFORMATION; TEXTURE ANALYSIS METHOD; TEXTURE CLASSIFICATION; WAVELET COEFFICIENTS; WAVELET PACKET BASIS; WAVELET SIGNATURES; WAVELET TEXTURE ANALYSIS;

CHEMICAL ANALYSIS; CHEMICAL PLANTS; CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; IMAGE PROCESSING; REAL TIME SYSTEMS; SYSTEMS ENGINEERING; TEXTURES;

FEATURE EXTRACTION;

EID: 80053289560     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2011.09.004     Document Type: Article

References (33)

1. Bharati M.H., Liu J.J., MacGregor J.F. Image texture analysis: methods and comparisons. Chemometrics Intell. Lab. Syst. 2004, 72(1):57-71.
2. Chang T., Kuo C.C.J. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 1993, 2(4):429-441.
3. Cocchi M., Seeber R., Ulrici A. WPTER: wavelet packet transform for efficient pattern recognition of signals. Chemometrics Intell. Lab. Syst. 2001, 57(2):97-119.
4. Coifman R.R., Wickerhauser M.V. Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 1992, 38(2):713-718.
5. Dong Y.J., Ma Wavelet-based image texture classification using local energy histograms. IEEE Signal Process. Lett 2011, 18(4):247-250.
6. Goldberg D. Genetic Algorithms in Search, Optimization, and Machine Learning 1989, Addison-Wesley Reading, Menlo Park.
7. Grigorescu S., Petkov N., Kruizinga P. Comparison of texture features based on Gabor filters. IEEE Trans. Image Process. 2002, 11(10):1160-1167.
8. Karabatak M., Ince M.C., Sengur A. Wavelet domain association rules for efficient texture classification. Appl. Soft Comput. 2011, 11(1):32-38.
9. Kempeneers P., De Backer S., Debruyn W., Scheunders P. Wavelet based feature extraction for hyperspectral vegetation monitoring. Proc. SPIE 2004, 5238:297-305.
10. Kim D., Han C., Liu J.J. Optimal wavelet packets for characterizing surface quality. Ind. Eng. Chem. Res. 2009, 48(5):2590-2597.
11. Kim D., Liu J.J., Han C. Quality determination of steel surfaces based on best feature selection. J. Chem. Eng. Jpn. 2011, 44(0):494-501.
12. Kulikowski, Tolhurst J.D. Psychophysical evidence for sustained and transient detectors in human vision. J. Physiol. 1973, 232(1):149.
13. Kumar S., Ghosh J., Crawford M.M. Best-bases feature extraction algorithms for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sensing 2001, 39(7):1368-1379.
14. Laine A., Fan J. Texture classification by wavelet packet signatures. IEEE Trans. Pattern Anal. Mach. Intell. 1993, 15(11):1186-1191.
15. Lee J., Yoo C., Lee I. Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chem. Eng. Sci. 2004, 59(14):2995-3006.
16. Lee M., Park S. A new scheme combining neural feedforward control with model-predictive control. AIChE Journal 2004, 38(2):193-200.
17. Li X., Nie P., Qiu Z.-J., He Y. Using wavelet transform and multi-class least square support vector machine in multi-spectral imaging classification of Chinese famous tea. Expert Syst. Appl. 2011, 38(9):11149-11159.
18. Lin, S.J., Hung Y.T., Hwang W.J. Efficient hardware architecture based on generalized Hebbian algorithm for texture classification. Neurocomputing, in press, doi:. http://10.1016/j.neucom.2011.05.010.
19. Liu J.J., Kim D., Han C. Use of wavelet packet transform in characterization of surface quality. Ind. Eng. Chem. Res. 2007, 46(15):5152-5158.
20. McGrath R. Image sensor technology. Single-Photon Imaging 2011, 160:27-47.
21. Pudil P., Novoviova J., Kittler J. Floating search methods in feature selection. Pattern Recognition Lett. 1994, 15(11):1119-1125.
22. Qin S. Statistical process monitoring: basics and beyond. J. Chemometrics 2003, 17(8-9):480-502.
23. Rajpoot N. Local discriminant wavelet packet basis for texture classification. Proc. SPIE 2003, 5207:774-783.
24. Reis M.S., Bauer A. Image-based classification of paper surface quality using wavelet texture analysis. Comput. Chem. Eng. 2010, 34(12):2014-2021.
25. Saito N., Coifman R.R. Local discriminant bases and their applications. J. Math. Imaging Vision 1995, 5(4):337-358.
26. Serpico S., Bruzzone L. A new search algorithm for feature selection in hyperspectralremote sensing images. IEEE Trans. Geosci. Remote Sensing 2001, 39(7):1360-1367.
27. Stutzle T., Hoos H. MAX-MIN ant system. Future Gener. Comput. Syst. 2000, 16(8):889-914.
28. Tan K.P., Du Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification. Chin. Opt. Lett. 2011, 9(1):011003.
29. Torras C., Puig D., Angel Garcia M. A new method to quantify parameters of membrane morphology from electron microscopy micrographs by texture recognition. Chem. Eng. Sci. 2011, 66(20):4582-4594.
30. Tsiaparas N.N., Golemati S., Andreadis I., Stoitsis J.S., Valavanis I., Nikita K.S. Comparison of multiresolution features for texture classification of carotid atherosclerosis from b-mode ultrasound. IEEE Trans. Inf. Technol. Biomed. 2011, 15(1):130-137.
31. Walczak B., Massart D.L. Wavelet packet transform applied to a set of signals: a new approach to the best-basis selection. Chemometrics Intell. Lab. Syst. 1997, 38(1):39-50.
32. Wang F., Liu B., Jiao A., Zhu X., Sun J., Li Y. Analysis of transparent coating technology on the surface texture of ash veneer based on wavelet component parameters. Adv. Electr. Electron. Eng. 2011, 547-554.
33. Zhang Y., Teng Y. Complex process quality prediction using modified kernel partial least squares. Chem. Eng. Sci. 2010, 65(6):2153-2158.