Performance analysis of colour descriptors for parquet sorting

Expert Systems with Applications

Volumn 40, Issue 5, 2013, Pages 1636-1644

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

^a UNIVERSITY OF PERUGIA   (Italy)

^b UNIVERSITY OF VIGO   (Spain)

Author keywords

Colour descriptors; Grading; Parquet; Sorting; Wood

Indexed keywords

CIE LAB; COLOUR SPACES; DESCRIPTORS; PARQUET; PERFORMANCE ANALYSIS; REALTIME PROCESSING; STATISTICAL DESCRIPTORS; STATISTICALLY SIGNIFICANT DIFFERENCE; VISUAL APPEARANCE;

EXPERT SYSTEMS; GRADING; GRAPHIC METHODS; HARDWOODS; SORTING; STATISTICAL METHODS; WOOD;

COLOR;

EID: 84872006100     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.09.007     Document Type: Article

References (62)

1. Åström, A., Åstrand, E., & Johansson, M. (1999). Automatic parquet block sorting using real-time spectral classification. In Real-time imaging IV. Vol. 3645 of proceedings of SPIE (pp. 76-84). California, USA: San Jose.
2. Arden, T. (1991). Color sorting of lumber. US Patent No. 4992949.
3. BabelColor, 2012. The ColorChecker (since 1976!). Available from: < http://www.babelcolor.com/main-level/ColorChecker.htm#ColorChecker-data >. Page visited on June 1, 2012.
4. Bianconi, F., Saetta, S., Sacchi, G., Asdrubali, F., & Baldinelli, G. (2011b). Colour calibration of an artificial vision system for industrial applications: Comparison of different polynomial models. In M. Rossi (Ed.), Colour and colorimetry multidisciplinary contributions. No. 21 in optics and photonics series notebooks (pp. 18-25). Maggioli Editore.
5. Bianconi, F., González, E., Fernández, A., & Saetta, S. A. (2012a). Apparatus to acquire a plurality of superficial images of at least one body and related method. IT Patent Application No. MI2012A001299. Filed on July 25, 2012.
6. F. Bianconi, A. Fernández, E. González, D. Caride, and A. Calviño Rotation-invariant colour texture classification through multilayer CCR Pattern Recognition Letters 30 8 2009 765 773
7. F. Bianconi, E. González, A. Fernández, and S.A. Saetta Automatic classification of granite tiles through colour and texture features Expert Systems with Applications 39 12 2012 11212 11218
8. 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 4 2011 043006-1 043006-17
9. V. Bombardier, and E. Schmitt Fuzzy rule classifier: Capability for generalization in wood color recognition Engineering Applications of Artificial Intelligence 23 6 2010 978 988
10. B.H. Bond, D.E. Kline, and P.A. Araman Differentiating defects in red oak lumber by discriminant analysis using color, shape, and density Wood and Fiber Science 34 4 2002 516 528 (Pubitemid 35354407)
11. C. Brunner, A. Maristany, D. Butler, D. VanLeeuwen, and J. Funck An evaluation of color spaces for detecting defects in Douglas-fir veneer Industrial Metrology 2 1992 169 184
12. B. Buchelt, and A. Wagenführ Evaluation of colour differences on wood surfaces European Journal of Wood and Wood Products 70 2012 389 391
13. M. Castellani, and H. Rowlands Evolutionary artificial neural network design and training for wood veneer classification Engineering Applications of Artificial Intelligence 22 4-5 2009 732 741
14. J. Ciccotelli, and J.-F. Portala Applications of artificial vision in the wood industry Industrial Metrology 2 1992 185 194
15. R.W. Conners, T.-H. Cho, C.T. Ng, T.H. Drayer, P.A. Araman, and R.L. Brisbin A machine vision system for automatically grading hardwood lumber Industrial Metrology 2 1992 317 342
16. R.W. Conners, C.W. McMillin, and K. Lin Identifying and locating surface defects in wood: Part of an automated lumber processing system IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5 6 1983 573 583 (Pubitemid 14503881)
17. M. Crosier, and L.D. Griffin Using basic image features for texture classification International Journal of Computer Vision 88 2010 447 460
18. DIN-1611 (2002). Sawn timber-Appearance grading of softwood. Part 1: European spruces, firs, pines, douglas firs and larches. Deutsches Institut für Normung.
19. DIN-EN-975-1 (2011). Sawn timber-Appearance grading of hardwoods. Part 1: Oak and beech. Deutsches Institut für Normung.
20. DIN-EN-975-2 (2004). Sawn timber-Appearance grading of hardwoods. Part 2: Poplars. Deutsches Institut für Normung.
21. A. Drimbarean, and P.F. Whelan Experiments in colour texture analysis Pattern Recognition Letters 22 10 2001 1161 1167 (Pubitemid 32680169)
22. Faria, J., Martins, T., Ferreira, M., & Santos, C. (2008). A computer vision system for color grading wood boards using fuzzy logic. In Proceedings of the IEEE international symposium on industrial electronics (ISIE 2008) (pp. 345-350). Cambridge, UK.
23. F. García-Lamont, J. Cervantes, and A. López Recognition of mexican banknotes via their color and texture features Expert Systems with Applications 39 10 2012 9651 9660
24. I. Gu, H. Andersson, and R. Vicen Wood defect classification based on image analysis and support vector machines Wood Science and Technology 44 4 2010 693 704
25. Z. Guo, L. Zhang, and D. Zhang A completed modeling of local binary pattern operator for texture classification IEEE Transactions on Image Processing 19 6 2010 1657 1663
26. J.C. Hermanson, and A.C. Wiedenhoeft A brief review of machine vision in the context of automated wood identification systems IAWA Journal 32 2 2011 233 250
27. Hrčka, R. (2008). Colour modelling as a tool for wood grading. In Proceedings of COST E53-Quality control for wood and wood products (pp. 165-170). Delft, The Netherlands.
28. Jabo, S. (2011). Machine vision for wood defect detection and classification. Master's thesis, Chalmers University of Technology, Göteborg, Sweden.
29. U. Kandaswamy, S.A. Schuckers, and D. Adjeroh Comparison of texture analysis schemes under nonideal conditions IEEE Transactions on Image Processing 20 8 2011 2260 2275
30. H.R. Kang Computational color technology 2006 Spie Press
31. Kauppinen, H. (2000). A two stage defect recognition method for parquet slab grading. In Proceedings of the 15th International Conference on Pattern Recognition (Vol. 4, pp. 803-806). Barcelona, Spain.
32. D. Kline, C. Surak, and P. Araman Automated hardwood lumber grading utilizing a multiple sensor machine vision technology Computers and Electronics in Agriculture 41 1-3 2006 139 155 (Pubitemid 37417773)
33. E. Kose Modelling of colour perception of different age groups using artificial neural networks Expert Systems with Applications 34 3 2008 2129 2139 (Pubitemid 350298233)
34. S. Kukkonen, H. Kälviäinen, and J. Parkkinen Color features for quality control in ceramic tile industry Optical Engineering 40 2 2001 170 177 (Pubitemid 32272395)
35. W. Kurdthongmee Colour classification of rubberwood boards for fingerjoint manufacturing using a SOM neural network and image processing Computers and Electronics in Agriculture 64 2 2008 85 92
36. Kyllönen, J., & Pietikäinen, M. (2000). Visual inspection of parquet slabs by combining color and texture. In Proceedings of the IAPR workshop on machine vision applications (pp. 187-192). Tokyo, Japan.
37. Labati, R., Gamassi, M., Piuri, V., & Scotti, F. (2009). A low-cost neural-based approach for wood types classification. In Proc. of the international conference on computational intelligence for measurement systems and applications (pp. 199-203). Hong Kong, China.
38. K. León, D. Mery, F. Pedreschi, and J. León Color measurements in Lab units from RGB digital images Food Research International 39 10 2006 1084 1091 (Pubitemid 44767631)
39. L. Lepistö, I. Kunttu, and A. Visa Color-based classification of natural rock images using classifier combinations Lecture Notes in Computer Science 3540 2005 901 909 (Pubitemid 41422732)
40. L. Liu, P. Fieguth, D. Clausi, and G. Kuang Sorted random projections for robust rotation-invariant texture classification Pattern Recognition 45 2012 2405 2418
41. F. López, J.M. Valiente, J.M. Prats, and A. Ferrer Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression Pattern Recognition 41 5 2008 1744 1755
42. Lu, Q., Conners, R., Kline, D., & Araman, P. (1997). A real-time algorithm for color sorting edge-glued panel parts. In Proc. of the international conference on image processing (Vol. 1, pp. 822-825). Barcelona, Spain.
43. A. Lycken Comparison between automatic and manual quality grading of sawn softwood Forest Products Journal 56 4 2006 13 18 (Pubitemid 43745501)
44. T. Mäenpää, and M. Pietikäinen Classification with color and texture: Jointly or separately? Pattern Recognition 37 8 2004 1629 1640 (Pubitemid 38728577)
45. 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.
46. G. Paschos Perceptually uniform color spaces for color texture analysis: An empirical evaluation IEEE Transactions on Image Processing 10 6 2001 932 937 (Pubitemid 32498467)
47. PG (2012). Parquet sorting. Code, data and results related to this paper. Available from: http://dismac.dii.unipg.it/parquet.
48. Pietikäinen, M., Nieminen, S., Marszalec, E., & Ojala, T. (1996). Accurate color discrimination with classification based on features distributions. In Proceedings of the 13th international conference on pattern recognition (ICPR'96) (Vol. 3, pp. 833-838). Vienna, Austria.
49. V. Piuri, and F. Scotti Design of an automatic wood types classification system by using fluorescence spectra IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40 3 2010 358 366
50. H. Po-Chieh Colorimetric calibration in electronic imaging devices using a look-up-table model and interpolations Journal of Electronic Imaging 2 1 1993 53 61
51. I. Qazi, O. Alata, J. Burie, A. Moussa, and C.F. Maloigne Choice of a pertinent color space for color texture characterization using parametric spectral analysis Pattern Recognition 44 1 2011 16 31
52. M.O. Rahman, A. Hussain, E. Scavino, H. Basri, and M.A. Hannan Intelligent computer vision system for segregating recyclable waste papers Expert Systems with Applications 38 2011 10398 10407
53. Rožman, D., Brezak, M., & Petrovic, I. (2006). Parquet sorting and grading based on color and texture analyses. In Proceedings of the IEEE international symposium on industrial electronics (Vol. 1, pp. 655-660). Montreal, Canada.
54. R. Schettini Colorimetric calibration of color scanners by back-propagation Pattern Recognition Letters 16 10 1995 1051 1056 (Pubitemid 26442214)
55. Schmidheiny, K. (2012). The bootstrap. In Short guides to microeconometrics. Universität Basel. Available from: http://kurt. schmidheiny.name/teaching/bootstrap2up.pdf. Page visited on July 26, 2012.
56. Schmitt, E. (2007). Contribution au système d'information d'un produit "bois". Appariement automatique de pièces de bois selon des critères de couleur et de texture. Ph.D. Thesis, Faculté des Sciences & Techniques, Université Henri Poincaré Nancy-I. Available from: http://tel.archives-ouvertes.fr/docs/00/17/01/06/PDF/Memoire- These-Emmanuel-SCHMITT-V2.pdf.
57. T. Schnabel, B. Zimmer, and A.J. Petutschnigg On the modelling of colour changes of wood surfaces European Journal of Wood and Wood Products 67 2009 141 149
58. P. Sobey, and E. Semple Detection and sizing visual features in wood using tonal measures and a classification algorithm Pattern Recognition 22 4 1989 367 380 (Pubitemid 20613736)
59. M.J. Swain, and D.H. Ballard Color indexing International Journal of Computer Vision 7 1 1991 11 32
60. P. Vienonen, A. Asikainen, and J. Eronen Color grading of beech parquet blocks by using spectral data Forest Products Journal 52 2002 49 52 (Pubitemid 34326192)
61. F. Wang Confidence interval for the mean of non-normal data Quality and Reliability Engineering International 17 2001 257 267
62. G. Wyszecki, and W. Styles Color science Concepts and methods, quantitative data and formulae second ed. 1982 Wiley-Interscience