Automatic classification of granite tiles through colour and texture features

Expert Systems with Applications

Volumn 39, Issue 12, 2012, Pages 11212-11218

  Bianconi, Francesco ^a,b   González, Elena ^b   Fernández, Antonio ^b   Saetta, Stefano A ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

^b UNIVERSITY OF VIGO   (Spain)

Author keywords

Classification; Colour; Grading; Granite; Texture

Indexed keywords

AUTOMATIC CLASSIFICATION; GOVERNING PARAMETERS; POSSIBLE SOLUTIONS; ROBUSTNESS AGAINST NOISE; TEXTURAL FEATURE; TEXTURE FEATURES; VISUAL FEATURE;

CLASSIFICATION (OF INFORMATION); COLOR; COMPUTER VISION; EXPERT SYSTEMS; GRADING; TEXTURES;

GRANITE;

EID: 84861187781     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.03.052     Document Type: Article

References (50)

1. M. Araújo, J. Martínez, C. Ordóñez, and J.A. Vilán Identification of granite varieties from colour spectrum data Sensors 10 9 2010 8572 8584
2. V. Arvis, C. Debain, M. Berducat, and A. Benassi Generalization of the cooccurrence matrix for colour images: Application to colour texture classification Image Analysis and Stereology 23 3 2004 63 72
3. M. Bennamoun, and A. Bodnarova Digital image processing techniques for automatic textile quality control Systems Analysis Modelling Simulation 43 11 2003 1581 1614
4. Bianconi, F.; Fernández, A. (2006). Granite texture classification with Gabor filters. In Proceedings of the 18th international congress on graphical engineering (XVIII INGEGRAF). Sitges, Spain.
5. F. Bianconi, and A. Fernández Evaluation of the effects of Gabor filter parameters on texture classification Pattern Recognition 40 12 2007 3325 3335
6. F. Bianconi, R. Harvey, P. Southam, and A. Fernández Theoretical and experimental comparison of different approaches for color texture classification Journal of Electronic Imaging 20 2011 043006-1-17
7. F. Bianconi, and A. Fernández On the occurrence probability of local binary patterns: a theoretical study Journal of Mathematical Imaging and Vision 40 3 2011 259 268
8. C. Boukouvalas, J. Kittler, R. Marik, and M. Petrou Perceptual correction for colour grading of random textures Machine Vision and Applications 12 3 2000 129 136
9. J. Cant, M. Large, L. McCall, and M. Goodale Independent processing of form, colour, and texture in object perception Perception 37 1 2008 57 78
10. Carrino, L.; Polini, W.; Turchetta, I. (2002). An automatic visual system for marble tile classification. In Proceedings of the institution of mechanical engineers, part B: Journal of engineering manufacture. (Vol. 216, pp. 1095-1108).
11. C. Cavina-Pratesi, R. Kentridge, C. Heywood, and A. Milner Separate channels for processing form, texture, and color: Evidence from fMRI adaptation and visual object agnosia Cerebral Cortex 20 10 2010 2319 2332
12. O. Chapelle, P. Haffner, and V. Vapnik Support vector machines for histogram-based image classification IEEE Transactions on Neural Networks 10 5 1999 1055 1064
13. F. van der Heijden, R. Duin, D. de Ridder, and D. Tax Classification, parameter estimation and state estimation An engineering approach using matlab 2004 John Wiley & Sons
14. G. Dislaire, E. Pirard, and M. Vanrell Marble classification using scale spaces R. Přikryl, Dimension stone 2004: New perspectives for a traditional building material 2004 Taylor & Francis Group Prague (Czech Republic)
15. H. Dogan, and O. Akay Using adaboost classifiers in a hierarchical framework for classifying surface images of marble slabs Expert Systems with Applications 37 12 2010 8814 8821
16. S. Ershad Color texture classification approach based on combination of primitive pattern units and statistical features The International Journal of Multimedia and Its Applications 3 3 2011 1 13
17. A. Drimbarean, and P.F. Whelan Experiments in colour texture analysis Pattern Recognition Letters 22 10 2001 1161 1167
18. R.O. Duda, P.E. Hart, and D.G. Stork Pattern classification 2nd ed. 2001 Wiley-Interscience
19. EN12440, (2009). EN 12440:2008 Natural stone - denomination criteria. European Committee for Standardization (CEN).
20. A. Fernández, O. Ghita, E. González, F. Bianconi, and P.F. Whelan Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification Machine Vision and Applications 22 6 2011 913 926
21. O. Ghita, P.F. Whelan, T. Carew, and P. Nammalwar Quality grading of painted slates using texture analysis Computers in Industry 56 2005 802 815
22. GR-CLASS, (2012). Automatic classification of granite tiles through colour and texture features. Available online at http://dismac.dii.unipg.it/ granite.
23. D. Hand, and K. Yu Idiot's Bayes - not so stupid after all? International Statistical Review 69 3 2001 385 398
24. K. Hoang, W. Wen, A. Nachimuthu, and X.L. Jiang Achieving automation in leather surface inspection Computers in Industry 34 1997 43 54
25. Hsu, C.; Chang, C.; Lin, C. (2010). A practical guide to support vector classification. Tech. rep.; Department of Computer Science National Taiwan University. URL http://www.csie.ntu.edu.tw/∼cjlin.
26. Jiaoyan, A.; Di, L.; Xuefeng, Z. (2004). Combination of wavelet analysis and color applied to automatic color grading of ceramic tiles. In Proceedings of IEEE computer society 17th international conference on pattern recognition (ICPR'04). Vol. 3. (pp. 235-238).
27. S. Kukkonen, H. Kälviäinen, and J. Parkkinen Color features for quality control in ceramic tile industry Optical Engineering 40 2 2001 170 177
28. E.V. Kurmyshev, R.E. Sánchez-Yáñez, and A. Fernández Colour texture classification for quality control of polished granite tiles Proceedings of the third IASTED international conference on visualization Imaging and image processing (VIIP 2003) Vol. II 2003 ACTA Press 603 608
29. F. Lahajnar, and S. Kovacic Rotation-invariant texture classification Pattern Recognition Letters 24 9-10 2003 1151 1161
30. L. Lepistö, I. Kunttu, and A. Visa Rock image classification using color features in Gabor space Journal of Electronic Imaging 14 2005 040503-1-3
31. Z. Liang, X. Bingang, Z. Chi, and D. Feng Intelligent characterization and evaluation of yarn surface appearance using saliency map analysis, wavelet transform and fuzzy artmap neural network Expert Systems with Applications 39 4 2012 4201 4212
32. M. López, J. Martínez, J.M. Matías, J. Taboada, and J.A. Vilán Functional classification of ornamental stone using machine learning techniques Journal of Computational and Applied Mathematics 234 4 2010 1338 1345
33. J.D. Luis-Delgado, J. Martínez-Alajarín, and L.M. Tomás-Balibrea Classification of marble surfaces using wavelets Electronics Letters 39 9 2003 714 715
34. T. Mäenpää, and M. Pietikäinen Classification with color and texture: Jointly or separately? Pattern Recognition 37 8 2004 1629 1640
35. J. Maldonado, and M. Graña Recycled paper visual indexing for quality control Expert Systems with Applications 36 5 2009 8807 8815
36. J. Martínez-Alajarín, J.D. Luis-Delgado, and L.M. Tomás-Balibrea Automatic system for quality-based classification of marble textures IEEE Transactions on Systems, Man and Cybernetics 35 2005 488 497
37. MondialMarmi, (2011). MondialMarmi: A granite image database for colour and texture analysis. Version 1.1. Available online at http://webs.uvigo.es/ antfdez/downloads.html.
38. L. Nanni, A. Lumini, and S. Brahnam Survey on LBP based texture descriptors for image classification Expert Systems with Applications 39 3 2012 3634 3642
39. Napoli, S. (2011). Stone Sector 2010. Italian Industry and International Trends. Tech. rep.; Internazionale Marmi e Macchine Carrara S.p.A.
40. Niskanen, M.; Silvén, O.; Kauppinen, H. (2001). Color and texture based wood inspection with non-supervised clustering. In proceedings of the 12th scandivanian conference on image analysis (SCIA 2001). (pp. 336-342). Bergen, Norway.
41. C. Palm Color texture classification by integrative co-occurrence matrices Pattern Recognition 37 5 2004 965 976
42. G. Paschos Perceptually uniform color spaces for color texture analysis: An empirical evaluation IEEE transactions on Image Processing 10 6 2001 932 937
43. B. Schölkopf, and A. Smola Learning with Kernels Support vector machines, regularization, optimization, and beyond 2002 MIT Press
44. D. Semnani, and M. Sheikhzadeh New intelligent method of evaluating the regularity of weft-knitted fabrics by computer vision and grading development Textile Research Journal 79 17 2009 1578 1587
45. Topalova, I.; Tzokev, A. (2010). Automated texture classification of marble shades with real-time PLC neural network implementation. In The 2010 international joint conference on Neural networks (IJCNN). (pp. 1-8).
46. S. Theodoridis, and K. Koutroumbas Pattern Recognition 3rd ed. 2006 Academic Press
47. M. Turtinen, M. Pietikäinen, and O. Silvén Visual characterization of paper using isomap and local binary patterns IEICE - Transactions on Information and Systems E89-D 7 2006 1 8
48. Franc, V.; Hlävac, V. (2004). Statistical pattern recognition toolbox for matlab. User's guide. Tech. rep.; Czech Technical University, Center for Machine Perception.
49. C. Yeh, and D.-B. Perng Establishing a demerit count reference standard for the classification and grading of leather hides The International Journal of Advanced Manufacturing Technology 18 10 2001 731 738
50. X. Zhang, Y. Ding, Y. Lv, A. Shi, and R. Liang A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM Expert Systems with Applications 38 5 2011 5930 5939