Integration of colour and textural information in multivariate image analysis: Defect detection and classification issues

Journal of Chemometrics

Volumn 21, Issue 1-2, 2007, Pages 10-23

  Prats Montalbán, José M ^a   Ferrer, A ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Experimental design; Logistic regression; Multivariate image analysis (MIA); RGB images; Texture

Indexed keywords

CLASSIFICATION (OF INFORMATION); COST EFFECTIVENESS; IMAGE ANALYSIS; MULTIVARIANT ANALYSIS; REGRESSION ANALYSIS; STATISTICAL PROCESS CONTROL; STATISTICS;

COLOR INFORMATION; COLOUR IMAGE; DEFECT CLASSIFICATION; DEFECT DETECTION; LOGISTICS REGRESSIONS; MULTIVARIATE IMAGE ANALYSE; MULTIVARIATE IMAGE ANALYSIS; RGB COLORS; RGB IMAGES; TEXTURAL INFORMATION;

COLOR;

EID: 34547904212     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1026     Document Type: Article

References (45)

1. Song KY, Kittler J, Petrou M. Defect detection in random colour textures. Image Vis. Comput. 1996; 14: 667-683.
2. Mäenpää T, Viertola J, Pietikäinen M. Optimising colour and texture features for real-time inspection. Pattern Anal. Appl. 2003; 6: 169-175.
3. Xie X, Mirmehdi M. Localising surface defects in random colour textures using multiscale texem analysis Proceedings of the 12th IEEE International Conference on Image Processing. (ICIP2005) Genova 2005; 1124-1127.
4. Geladi P, Grahn H. Multivariate Image Analysis. Wiley: Chichester, England, 1996.
5. Bharati MH, MacGregor JF. Multivariate image analysis for process monitoring and control. Ind. Eng. Chem. Res. 1998; 37: 4715-4724.
6. Bharati MH, MacGregor JF, Tropper W. Softwood lumber grading through on-line multivariate image analysis techniques. Ind. Eng. Chem. Res. 2003; 42: 5345-5353.
7. Yu H, MacGregor JF, Haarsma G, Bourg W. Digital imaging for on-line monitoring and control of industrial snack food processes. Ind. Eng. Chem. Res. 2003; 42: 3036-3044.
8. Liu JJ, Bharati MH, Dunn KG, MacGregor JF. Automatic masking in multivariate image analysis using support vector machines. Chemometr. Intell. Lab. Syst. 2005; 79: 42-54.
9. Bharati MH, MacGregor JF. Texture analysis of images using Principal Component Analysis, SPIE/Photonics Conference on Process Imaging for Automatic Control, Boston, MA, 2000; 27-37.
10. Huang J, Esbensen KH. Applications of the Angle Measure Technique (AMT) in image analysis Part 1. A new methodology for in situ powder characterization. Chemometr. Intell. Lab. Syst. 2000; 54: 1-19.
11. Bharati MH, Liu JJ, MacGregor JF. Image texture analysis: methods and comparisons. Chemometr. Intell. Lab. Syst. 2004; 72: 57-71.
12. Liu JJ, MacGregor JF. Estimation and monitoring of product aesthetics: application to manufacturing of 'engineered stone' countertops. Mach. Vis. Appl. 2006; 16: 374-383.
13. Lied TT, Geladi P, Esbensen K. Multivariate image regression (MIR): implementation of image PLSR-first forays. J. Chemometrics. 2000; 14: 585-598.
14. Lied TT, Esbensen K. Principles of MIR, multivariate image regression. I: regression typology and representative application studies. Chemometr. Intell. Lab. Syst. 2001; 58: 213-226.
15. Huang J, Esbensen KH. Applications of the Angle Measure Technique (AMT) in image analysis Part II: prediction of powder functional properties and mixing components using Multivariate AMT Regression (MAR). Chemometr. Intell. Lab. Syst. 2000; 54: 1-19.
16. Yu H, MacGregor JF. Multivariate image analysis and regression for prediction of coating and distribution in the production of snack foods. Chemometr. Intell. Lab. Syst. 2003; 72: 57-71.
17. Yu H, MacGregor JF. Monitoring turbulent nonpremixed flames in an industrial boiler using multivariate image analysis (MIA), Proceedings of the Seventeenth International Forum. Process Analytical Chemistry. January 21-24, 2003; 597-602.
18. Liu JJ, MagGregor JF. Modeling and optimization of product appearance: application to injection-molded plastic models. Ind. Eng. Chem. Res. 2005; 44: 4687-4696.
19. Bharati MH, MacGregor JF, Champagne M. Using near-infrared multivariate image regression to predict properties. Tappi J. 2004; 3(5): 8-14.
20. Eriksson L, Wold S, Trygg Johan. Multivariate analysis of congruent images (MACI). J. Chemometrics 2005; 19: 393-403.
21. Huang J, Wium H, Qvist KB, Esbensen KH. Multi-way methods in image analisis-relationships and applications. Chemometr. Intell. Lab. Syst. 2003; 66: 141-158.
22. Gurden SP, Lage EM, de Faria CG, Joekes I, Ferreira M. Analysis if video images from a gas-liquid transfer experiment: a comparison of PCA and PARAFAC for multivariate image analysis. J. Chemometrics 2003; 17: 400-412.
23. Liu JJ, MacGregor JF, Duchesne C, Bartolacci G. 2005; Flotation froth monitoring using multirresolutional multivariate image analysis. Miner. Eng. 18: 65-76.
24. Liu JJ, MacGregor JF. On the extraction of spectral and spatial information from images. Chemometr. Intell. Lab. Syst. 2006; 85: 119-130.
25. Bharati MH. Multivariate Image Analysis and Regression for Industrial Process Monitoring and Product Quality Control, Ph.D. Thesis, McMaster Univesity, Hamilton (Ontario), Canada, 2002.
26. Henrion R. N-way principal components analysis. Theory, algorithms and applications. Chemometr. Intell. Lab. Syst. 1994; 25: 1-23.
27. Bro R, Multilinear Calibration. Multilinear PLS. J. Chemometrics 1996; 10: 47-61.
28. Jackson JE. A User's guide to Principal Components. Wiley: New York, 1991.
29. Walczak B. Wavelets in Chemistry. Elsevier Science: Amsterdam, 2000.
30. Daubechies I, Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 1988; 41: 909-996.
31. Porter R, Canagarajah N. Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes. IEE Proc. Vis. Image Signal Pro. 1997; 144(3): 180-188.
32. Van der Wouwer G. Wavelets for multiscale texture analysis, PhD Thesis, University of Antwerpen, Antwerpen, Belgium, 1998.
33. Nomikos P, MacGregor JF. Multivariate SPC charts for monitoring batch processes. Technometrics 1995; 37(1): 41-59.
34. Tracy ND, Young JC, Mason RL. 1992; Multivariate control charts for individual observations. J. Qual. Technol. 24: 88-95.
35. Jackson JE, Mudholkar GS. Control procedures for residuals associated with principal component analysis. Technometrics 1979; 21(3): 341-349.
36. Wise BM, Gallagher N, Bro R, Shaver JM, Windig W, Scott KochR. MIA_Toolbox Version 1.0 for use with Matlab™ Manual, Eigenvector Research, Inc., WA, 2005.
37. Idilko EF, Friedman JH. Classification: oldtimers, newcomers. J. Chemometrics 1989; 3: 463-475.
38. Naes T, Indahl U. A unified description of classical classification methods for multicollinear data. J. Chemometrics 1998; 12: 205-220.
39. Wold S, Albano C, Dunn WJ, Edlund U, Esbensen K, Geladi P, Hellberg S, Johansson E, Lindberg W, Sjöström M. Multivariate data analysis in chemistry. In Chemometrics: Mathematics and Statistics in Chemistry D, Kowalski BR (ed.). Reidel Publishing Company: Dordrecht, Holland, 1984.
40. Sjöström M, Wold S, Söderström B. PLS Discriminant Plots, Proceedings of PARC in Practice, Amsterdam, June 19-21, 1985; Elsevier Science Publishers B.V.: North-Holland, 1986.
41. Wold H. PLS Regression. In Encycopledia of Statistical Sciences, Vol. 6. Johnson NL, Kotz S (eds). Wiley: New Cork, 1984; 581-591.
42. Atkinson AC, Donev AN. Optimum Experimental Designs. Oxford University Press: New York, 1992.
43. Jobson JD. Applied Multivariate Data Analysis: Categorical and Multivariate Methods. Springer-Verlag: Berlin, 1992.
44. Gurden SB, Westerhuis JA, Bro R, Smilde AK. A comparison of multiway regression and scaling methods. Chemometr. Intell. Lab. Syst. 2001; 59(1-2): 121-136.
45. López F, Valiente JM, Prats-Montalban JM, Ferrer A. Feature Selection using Experimental Design and Logistic Regression: Application to Surface Grading. submitted to Pattern Recognition, 2006.