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Applying multifractal spectrum combined with fractal discrete Brownian motion model to wood defects recognition

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Abstract

Wood nondestructive testing technology is a new and multidisciplinary industry scientific research. It has attained fast development and achievements in recent years. X-ray computed tomography (CT) scanning technology is a kind of wood nondestructive testing technology in practice. CT scanning technology has been applied to the detection of internal defects in the logs for the purpose of obtaining prior information, which can be used to reach better wood sawing decision. Fractal geometry and its extension multifractal are used for describing, modeling, analyzing, and processing of different complex shapes and images. A method in CT image edge detection using multifractal theory combined with fractal Brownian motion is applied in the paper. First, its multifractal spectrum is estimated. Then, different types of pixels are classified by the spectrum; they are smoothing edge points and singular edge points. From the images processed by multifractal spectrum theory and compared with each image by different spectrum values, it can be seen that the larger the range of threshold is set, the more exact the edge can be detected. The paper provides a new method to recognize the defect information and to saw it in the condition of nondestructive wood.

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References

  • Arbeiter M, Patzschke N (1996) Random self-similar measures. J Math 181:5–42

    Google Scholar 

  • Canus C (1998) Robust large deviation multifractal spectrum estimation. Proceed Int Wave Conf Tangier 1998:739–742

    Google Scholar 

  • Dai F, Xu X (2002) A method determining vertical scaling parameters of fractal interpolation. CADDM 12(1):37–41

    Google Scholar 

  • Falconer K (1996) Techniques in fractal geometry. Wiley, London

    Google Scholar 

  • Funck JW, Zhang Y, Butler DA, Brunner CC, Forrer JB (2003) Image segmentation algorithms applied to wood defect detection. Comput Electron Agr 41:157–179

    Article  Google Scholar 

  • Gonzalez RC (2003) Digital image processing, 2nd edn. Prentice Hall Inc, USA

    Google Scholar 

  • Hodges DG, Anderson WC, McMillin CW (1990) The economic potential of CT scanners for hardwood sawmills. Forest Prod J 40(3):65–69

    Google Scholar 

  • Liu S (2001) The integral formula for calculating the Hausdorff measure of some fractal sets. Approx Th Appl 17(1):70–75

    CAS  Google Scholar 

  • Mandelbrot BB (1983) The fractal geometry of nature. Freeman, New York

    Google Scholar 

  • Miller BK (1991) Peach defect detection with machine vision. Trans ASAE 34(6):2588–2597

    Google Scholar 

  • Morigi MP, Casali F, Bettuzzi M, Bianconi D, Brancaccio R, Cornacchia S (2007) CT investigation of two paintings on wood tables by Gentile da Fabriano. Nucl Instrum Meth A 580(1):735–738

    Article  CAS  Google Scholar 

  • Occeña LG, Schmoldt DL (1996) GRASP—a prototype interactive graphic sawing program. Forest Prod J 46(11/12):40–42

    Google Scholar 

  • Reljin IS, Reljin BD (2002) Fractal geometry and multifractals in analyzing and processing medical data and images. Fractals 10(4):283–293

    Google Scholar 

  • Tan T, Yan H (2001) Object recognition based on fractal neighbor distance. Signal Process 10:202–208

    Google Scholar 

  • Thawornwong S, Occeña LG, Schmoldt DL (2003) Lumber value differences from reduced CT spatial resolution and simulated log sawing. Comput Electron Agr 41:23–43

    Article  Google Scholar 

  • Zhang J (2004) Fractal. Tsinghua university press, Beijing, p 2004

    Google Scholar 

Download references

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Correspondence to Dawei Qi.

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Yu, L., Qi, D. Applying multifractal spectrum combined with fractal discrete Brownian motion model to wood defects recognition. Wood Sci Technol 45, 511–519 (2011). https://doi.org/10.1007/s00226-010-0341-7

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  • DOI: https://doi.org/10.1007/s00226-010-0341-7

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