A novel generalization of the gray-scale histogram and its application to the automated visual measurement and inspection of wooden Pallets

Image and Vision Computing

Volumn 25, Issue 6, 2007, Pages 805-816

  Patricio, M A ^a   Maravall, D ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Automatic visual inspection; Defect detection; Generalized gray level histogram; Image segmentation; Texture recognition; Wood inspection

Indexed keywords

HEURISTIC METHODS; INSPECTION; LIGHTING; WOOD PRODUCTS;

HISTOGRAMS; VISUAL MEASUREMENT; WOODEN PALLETS;

COMPUTER VISION;

HEURISTIC METHODS; ILLUMINATION; INSPECTION; WOOD PRODUCTS;

EID: 34047253025     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2006.05.020     Document Type: Article

References (42)

1. Huber H.A., McMillin C.W., and McKinney J.P. Lumber defect detection abilities of furniture rough mill employees. Forest Production Journal 35 11/12 (1985) 79-82
2. Polzleitner W., and Schwingshakl G. Real-time surface grading of profiled wooden boards. Industrial Metrology 2 (1992) 283-298
3. Levine M.D. Vision in Man and Machine (1985), McGraw Hill, New York
4. Cho T.H., Conners R.W., and Araman P.A. A computer vision system for automated grading of rough hardwood lumber using a knowledge-based approach. Proceedings of the IEEE International Conference on Systems, Man and Robotics (1990) 345-350
5. Astrom A. High speed industrial image processing applied to the wood industry. Proceedings of 12th Scandinavian Conference on Image Analysis 319 (2001) 770-785
6. Ciccotelli J., and Portala J.F. Application of artificial vision in the wood industry. Industrial Metrology 2 (1992) 185-194
7. Conners R.W., Cho T.H., Ng C.T., and Drayer T.H. A machine vision system for automatically grading hardwood lumber. Industrial Metrology 2 (1992) 317-342
8. Ersboll B.K., and Conradsen K. Automated grading of wood slabs: the development of a prototype system. Industrial Metrology 2 (1992) 219-236
9. Holyen S., and Tengs A. Lumber grading with optical scanning devices. Proceedings of the 3rd International Conference on Scanning Technology in Sawmilling XVII (1989), Miller Freeman Publication, San Francisco 1-10
10. Hu C., Tanaka C., and Ohtani T. Locating and identifying splits and holes on sugi by the laser displacement sensor. Journal of Wood Science 49 6 (2003) 492-498
11. Kabir M.F., Schmoldt D.L., and Schafer M.E. Time domain ultrasonic signal characterization for defects in thin unsurfaced hardwood lumber. Wood and Fiber Science 34 1 (2002) 165-182
12. Kabir M.F., Schmoldt D.L., Araman P.A., Schafer M.E., Mark L., and Sang-Mook. Classifying defects in pallet stringers by ultrasonic scanning. Wood and Fiber Science 35 3 (2003) 341-350
13. Klinkhachorn P., Kothari R., Huber H.A., McMillin C.W., Mukherjee K., and Barnekov V. Prototyping an automated lumber processing system. Forest Production Journal 35 11 (1993) 79-82
14. Lampinen J., and Smolander S. Selforganising feature extraction in recognition of wood surface defects and color images. International Journal of Pattern Recognition and Artificial Intelligence 10 2 (1996) 97-113
15. Malasne N., and Paindavoine M. Architecture for real-time wood inspection. Proceedings of the SPIE The International Society for Optical Engineering 4116 (2000) 429-435
16. McDonald K.A. Defect detection in lumber: state of the art. Forest Production Journal 31 1 (1981) 33-34
17. Niskanen M., Kauppinen H., and Silven O. Real-time aspects of SOM-based visual surface inspection. Proceedings of SPIE - The International Society for Optical Engineering 46 (2002) 123-134
18. Packianather M.S., and Drake P.R. Neural networks for classifying images of wood veneer. Part 2. The International Journal of Advanced Manufacturing Technology 16 6 (2000) 424-433
19. Schmoldt D.L., He J., and Abbott A.L. Automated labeling of log features in CT imagery of multiple hardwood species. Wood Fiber Science 32 3 (2000) 287-300
20. Wyckhuyse A., and Maldague X. A study of wood inspection by infrared thermography, Part II: Thermography for wood defects detection. Research in Nondestructive Evaluation 13 1 (2001) 13-21
21. Zhu D., Conners R.W., Schmidt D.L., and Araman P.A. A prototype vision system for analysing CT imagery of hardwood logs. IEEE Transactions on Systems, Man and Cybernetics. Part B - Cybernetics 26 4 (1996) 522-532
22. Rosandich R.G. Intelligent Visual Inspection (1997), Chapman Hall, London
23. C. Neubauer, Fast detection and classification of defects on treated metal surfaces using a backpropagation neural network, in: Proceedings of the International Joint Conference on Neural Networks, 1992, pp. 1148-1153.
24. Kumar A., and Pang G.K.H. Defect detection in textured materials using optimized filters. IEEE Transactions Systems, Man, and Cybernetics - Part B 32 5 (2002) 553-570
25. Moring I., and Silven O. Automatic visual wood inspection: experiments. STeP-86 2 (1986) 290-294
26. T. Cho, R.W. Conners, P.A. Araman, A computer vision system for analyzing images of rough hardwood lumber, in: Proceedings of the 10th International Conference on Pattern Recognition, Atlantic City, NJ, vol. 1, 1990, pp. 726-728.
27. Jasper W.J., Gamier S.J., and Potlapalli H. Texture characterization and defect detection using adaptive wavelets. Optical Engineering 35 11 (1996) 3140-3149
28. Escofet J., Navarro R., Millan M.S., and Pladelloreans J. Detection of local defects in textiles webs using Gabor filters. Optical Engineering 37 8 (1998) 2297-2307
29. Dunn D.F., and Higgins W.E. Optimal Gabor filters for texture segmentation. IEEE Transactions on Image Processing 7 (1995) 947-964
30. Kumar A., and Pang G. Defect detection in textured materials using Gabor filters. IEEE Transactions on Industry Applications 38 2 (2002) 425-440
31. Kim C.W., and Koivo A.J. Hierarchical classification of surface defects on dusty wood boards. Pattern Recognition Letters 7 (1994) 713-721
32. Pham D.T., and Alcock R.J. Recent advances in intelligent inspection of woods boards. 13th International Conference on Applications of Artificial Intelligence in Engineering (1998), Computational Mechanics Publications, Southampton, UK 105-108
33. Silven O., and Kauppinen H. Recent developments in wood inspection. International Journal of Pattern Recognition and Artificial Intelligence 10 1 (1996) 83-95
34. Patricio M.A., and Maravall D. Wood texture analysis by combining the connected elements histogram and artificial neural networks. In: Mira J., and Prieto A. (Eds). Bio-inspired Applications of Connectionism, LNCS 20851 (2001), Springer, Berlin 160-167
35. Maravall D., and Patricio M.A. Image segmentation and pattern recognition: a novel concept, the histogram of connected elements. In: Chen D., and Cheng X. (Eds). Pattern Recognition and String Matching, Pattern Recognition and String Matching (2002), Kluwer Academic Publishers, Dordrecht 399-452
36. Soille P. Morphological Image Analysis. 2nd ed. (2003), Springer, Berlin
37. Tsai R. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation RA-3 4 (1987) 323-344
38. Hemayed, E.E., A survey of self-camera calibration, in: Proceedings of the IEEE Conference on Advance Video and Signal based Surveillance, Florida, 2003, pp. 351-358.
39. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. 2nd ed. (2000), Wiley, London
40. Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society Series B 48 3 (1985) 259-302
41. Glasbey C.A., and Horgan G.W. Image Analysis for the Biological Sciences (1995), John Wiley, New York
42. Kitler J. Probabilistic relaxation and the hough transform. Pattern Recognition 33 (2000) 705-714