PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2008 | 18 | 4 | 581-592
Tytuł artykułu

Local detection of defects from image sequences

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Our aim is to discuss three approaches to the detection of defects in continuous production processes, which are based on local methods of processing image sequences. These approaches are motivated by and applicable to images of hot metals or other surfaces, which are uniform at a macroscopic level, when defects are not present. The first of them is based on the estimation of fractal dimensions of image cross-sections. The second and third approaches are compositions of known techniques, which are selected and tuned to our goal. We discuss their advantages and disadvantages, since they provide different information on defects. The results of their testing on 12 industrial images are also summarized.
Rocznik
Tom
18
Numer
4
Strony
581-592
Opis fizyczny
Daty
wydano
2008
otrzymano
2007-12-09
poprawiono
2008-04-19
Twórcy
  • Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
autor
  • Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  • Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
  • Adler J.R. (1981). The Geometry of Random Fields, Wiley, Chichester.
  • Barnsley M. (1988). Fractals Everywhere, Academic Press, New York, NY.
  • Benassi A., Cohen S., Istas J. (2002). Identification and properties of real harmonizable fractional levy motions, Bernoulli 8(1): 97-115.
  • Benassi A., Cohen S., Istas J. (2003). Local self-similarity and Hausdorff dimension, Comptes Rendus Mathematique 336(3): 267-272.
  • Chan G., Hall P. and Poskitt D. S. (1995). Periodogram-based estimators of fractal properties, Annals of Statistics 23 (5): 1684-1711.
  • Conci A., Proenca C.B. (1998). A fractal image analysis system for fabric inspection based on a box-counting method. Computer Networks and ISDN Systems 30(20-21): 1887-1895.
  • Constantine A.G. and Hall P. (1994). Characterizing surface smoothness via estimation of effective fractal dimension, Journal of the Royal Statistical Society: Series B 56 (1): 97-113.
  • Davies E. R. (2005). Machine Vision: Theory, Algorithms, Practicalities, 3rd Edn., Academic Press, San Francisco, CA.
  • Davies E. R. (2008). A generalised approach to the use of sampling for rapid object location, International Journal of Applied Mathematics of Computer Science 18(1): 7-19.
  • Davies S. and Hall P. (1999). Fractal analysis of surface roughness by using spatial data, Journal of the Royal Statistical Society: Series B 61 (1): 3-37.
  • Dworkin S.B. and Nye T.J. (2006). Image processing for machine vision measurement of hot formed parts, Journal of Materials Processing Technology 174 (1-3): 1-6.
  • Falconer K. (1990). Fractal Geometry, Wiley, New York, NY.
  • Gonzalez R.C. and Wintz P. (1977). Digital Image Processing, Addison-Wesley, Reading, MA.
  • Hu M.K. (1962) Visual pattern recognition by moment invariants, IEEE Transactions on Information Theory 6(2): 179-187.
  • Kent J.T. and Wood A.T. (1997), Estimating the fractal dimension of a locally self-similar Gaussian process by using increments, Journal of the Royal Statistical Society: Series B 59 (3): 679-699.
  • Istas J. and Lang G. (1997). Quadratic variations and estimation of the local Hölder index of a Gaussian process, Annales de I'Institut Henri Poincare (B) Probability and Statistics 33 (4): 407-436.
  • Jähne, B. (2002). Digital Image Processing, Springer-Verlag, Berlin/Heidelberg.
  • Gill J. Y. and Werman M. (1993). Computing 2-D min, median and max filters, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(5): 504-507.
  • O'Leary P. (2005). Machine vision for feedback control in a steel rolling mill, Computers in Industry 56(8-9): 997-1004.
  • Malamas E.N., Petrakis E. G. M., Zervakis M., Petit L. and Legat J-D. (2003). A survey on industrial vision systems, applications and tools, Image and Vision Computing 21(2): 171-188.
  • Ott E. (1993). Chaos in Dynamical Systems, Cambridge University Press, Cambridge.
  • Rafajłowicz E. (2008). Testing homogeneity of coefficients in distributed systems with application to quality monitoring, IEEE Transactions on Control Systems Technology 16(2): 314-321.
  • Pratt P.K. (2001). Digital Image Processing, 3rd Edn., Wiley, New York, NY.
  • Rafajłowicz E. (2004) Testing (non-)existence of input-output relationships by estimating fractal dimensions, IEEE Transactions Signal Processing 52(11): 3151-3159.
  • Rosenfeld A. and Kak A.C. (1982). Digital Picture Processing, Academic Press, Inc., Orlando, FL.
  • Schuster H.G. (1988). Deterministic Chaos, VGH Verlagsgesellschaft, Weinheim.
  • Skubalska-Rafajłowicz E. (2005). A new method of estimation of the box-counting dimension of multivariate objects using space-filling curves, Nonlinear Analysis 63 (5-7): 1281-1287.
  • Skubalska-Rafajłowicz E. (2008). Local correlation and entropy maps as tools for detecting defects in industrial images, International Journal of Applied Mathematics and Computer Science 18(1): 41-47.
  • Tricot C. (1995). Curves and Fractal Dimension, Springer, New York, NY.
  • Tsai D.-M., Lin C.-T., Chen J.-F. (2003). The evaluation of normalized cross correlations for defect detection, Pattern Recognition Letters 24 (15): 2525-2535.
  • Wnuk M. (2008). Remarks on hardware implementation of image processing algorithms, International Journal of Applied Mathematics and Computer Science 18(1): 105-110.
  • Van Herk M. (1992). A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels, Pattern Recognition Letters 13(7): 517-521.
  • Vincent L. (1993). Grayscale area openings and closings, their efficient implementation and applications, Proceedings of the EURASIP Workshop on Mathematical Morphology and its Applications to Signal Processing, Barcelona, Spain, pp. 22-27.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv18i4p581bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.