Real-time image texture analysis in quality management using grid computing: an application to the MDF manufacturing industry
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Computer vision has arisen as one of the most important application areas in manufacturing processes. This work shows a new real-time texture analysis for medium-density fiberboard with melamine paper, using edge detection techniques and threshold detection methods. To minimize the time of identification of defects, the images of fiberboard are sent to a grid system. In a first phase, several tests are carried out using different image resolutions and sizes. In a second phase, to optimize the system, with the best resolution obtained and using a grid system, our aim is to minimize the time of detection of possible defects without jeopardizing the performance of the quality control system. Results show that, using accurate resolutions, the error detection process is quicker and the defect identification rate significantly improves.
KeywordsGrid computing Computer vision Texture analysis Edge detection
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- 1.Sonka M, Hlavac V, Boyle R (1999) Image processing analysis and machine vision. Chapman & Hall, LondonGoogle Scholar
- 4.Foster I, Kesselman C (2003) The grid 2: blueprint for a new computing infrastructure. Morgan Kaufmann, San MateoGoogle Scholar
- 5.Anderson D (2004) Boinc: a system for public-resource computing and storage. In: Fifth IEEE/ACM international workshop on grid computing, pp 4–10Google Scholar
- 9.Niskanen M, Silvn O, Kauppinen H (2001) Color and texture based wood inspection with non-supervised clustering. In: Proceedings of the 12th Scandinavian conference on image analysis (SCIA2001), pp 336–342Google Scholar
- 14.Murino V, Bicego M, Rossi I (2004) Statistical classification of raw textile defects. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 4, pp 311–314Google Scholar
- 17.Chen X, Jing H, Tao Y, Cheng X (2005) Real-time image analysis for nondestructive detection of metal slivers in packed food. In: Chen Y-R, Meyer GE, Tu S-I (eds) Optical sensors and sensing systems for natural resources and food safety and quality. Proceedings of the SPIE, volume 5996. SPIE, Bellingham, pp 120–129Google Scholar
- 18.Patel VC, McClendon RW, Goodrum JW (1998) Crack detection in eggs using computer vision and neural networks. Artif Intell Rev 8(2):21–31Google Scholar
- 20.Prasad BS, Sarcar MMM (2009) Experimental investigation to predict the condition of cutting tool by surface texture analysis of images of machined surfaces based on amplitude parameters. Int J Mach Machinability Mater 4(2–3):217–236Google Scholar
- 21.Yin Y, Lei J (2009) Prototype system of textile flaw detection based on wavelet reconstructions. J Inf Comput Sci 5(1):207–214Google Scholar