Skip to main content
Log in

Computing the Success Factors in Consistent Acquisition and Recognition of Objects in Color Digital Images by Explicit Preconditioning

The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The paper studies the factors influencing the consistent acquisition and recognition of object's color and border features in digital imaging. The proposed image acquisition process is utilized by a computer supported imaging system implementing the acquisition and analysis of skin lesion images supporting medical diagnosis. In addition the same approach may be used for several problems requiring reliable color measurement and object identification. Two methodologies are adopted: The Bayesian Networks, which provide an efficient way of reasoning under uncertainty and are used to incorporate the expert judgement into the estimation of the probability of successful operation, and a Markov chain approach, which is generally used for the dynamic modeling of the system behavior. The Markov chain model requires asymptotically the solution of sparse linear systems. Explicit preconditioned methods are used for the efficient solution of the derived sparse linear system, and the parallel implementation of the dominant computational part is exploited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. F. Aitken, J. Pfitzner, D. Battistutta, P. K. O'Rourke, A. C. Green, and N. G. Martin. Reliability of computer image analysis of pigmented skin lesions of Australian adolescents. Cancer, 78(2):252–257, 1996.

    Google Scholar 

  2. M. P. Bekakos and O. B. Efremides. An efficient parallel approach to reduce sparse matrices with invariant entries. In C.A. Brebbia and H. Power, eds., Fourth International Conference on Applications of High Performance Computing in Engineering, pp. 11–18. Computational Mechanics Publications, 1995.

  3. M. Benzi and M. Tuma. A parallel solver for large-scale Markov chains. Applied Numerical Mathematics, 41:135–153, 2002.

    Google Scholar 

  4. A. Bobbio, L. Portinale, M. Minichino, and E. Ciancarmela. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering and System Safety, 71(3):249–260, 2001.

    Google Scholar 

  5. S. Bologna, E. Giancamerla, M. Minichino, A. Bobbio, G. Franceschinis, L. Portinale, and R. Gaeta. Comparison of methodologies for the safety and dependability assessment of an industrial programmable logic controller. In N. Piccinini and E. Zio, eds., European Safety Dependability Conference (ESREL2001), pp. 411–418, A. A. Balkema Publications, 2001.

  6. Y. C. Chang and J. F. Reid. RGB calibration for color image analysis in machine vision. IEEE Transactions Image Processing, 5:1414–1422, 1996.

    Google Scholar 

  7. O. B. Efremides and M. P. Bekakos. Designing processor-time optimal systolic configurations. In M. P. Bekakos, ed., Highly Parallel Computations: Algorithms and Applications, Series: Advances in High Performance Computing, 5:339–364. WIT Press, 2001.

  8. G. Finlayson. Color in perspective. IEEE Trans. Pattern Anal. Machine Intell., 18:1034–1038, 1996.

    Google Scholar 

  9. G. Goldon and G. Nuckols. Interior Lighting for Designers. John Wiley, 1995.

  10. C. R. Gonzalez and E. R. Woods. Digital Image Processing. Addison Wesley, 1995.

  11. G. A. Gravvanis. Explicit preconditioned domain decomposition schemes. Inter. J. Computational and Numerical Analysis and Applications, 2(1):19–36, 2002.

    Google Scholar 

  12. G. A. Gravvanis. Explicit preconditioned generalized domain decomposition methods. Inter. J. Applied Mathematics, 4(1):57–71, 2000.

    Google Scholar 

  13. G. A. Gravvanis. Approximate inverse banded matrix techniques. Engineering Computations, 16(3):337–346, 1999.

    Google Scholar 

  14. G. A. Gravvanis. An approximate inverse matrix technique for arrowhead matrices. Inter. J. Comp. Math., 70:35–45, 1998.

    Google Scholar 

  15. G. A. Gravvanis. The rate of convergence of explicit approximate inverse preconditioning. Inter. J. Comp. Math., 60:77–89, 1996.

    Google Scholar 

  16. V. Jensen Finn. An Introduction to Bayesian Networks. UCL Press, 1996.

  17. H. Kang. Color Technology for Electronic Imaging Devices. SPIE Opt. Eng. Press, 1997.

  18. J. E. Kaufman. Lighting Handbook Application Volume. Illuminating Engineering Society of North America, 1987.

  19. V. Leemans, H. Magein, and M.-F. Destain. AE—automation and emerging technologies: On-line fruit grading according to their external quality using machine vision, Biosystems Engineering, 83(4):397–404, 2002.

    Google Scholar 

  20. B. P. Lester. The Art of Parallel Programming. Prentice-Hall Int. Inc., 1993.

  21. N. Limnios and G. Oprisan. Semi-Markov Processes and Reliability, Birkhäuser, 2001.

  22. I. Maglogiannis. A digital image acquisition system for skin lesions. In D. Chakraborty and E. Krupinski, eds., SPIE International Conference on Medical Imaging, pp. 337–346. SPIE Press 2003.

  23. I. Maglogiannis and E. Zafiropoulos. Characterization of dermatological images using support vector machines. In H. Arabnia, eds., International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp. 1293–1297. CSREA Press, 2003.

  24. M. Malhotra and K. Trivedi. Dependability modeling using Petri nets. IEEE Transactions on Reliability, 44(3):428–439, 1995.

    Google Scholar 

  25. M. Modarres, M. Kaminskiy, and V. Kritvtsov. Reliability engineering and risk Analysis. Marcel Dekker Inc., 1999.

  26. A. Pagès and M. Gondran. Fiabilitèdes Systèmes. Eyrolles, 1980.

  27. M. Pecht. Product Reliability, Maintainability, and Supportability Handbook. CRC Press, 1995.

  28. A. N. Platis. An extension of the Performability measure and application in system reliability. Inter. J. Computational and Numerical Analysis and Applications, 2(1):87–101, 2002.

    Google Scholar 

  29. A. N. Platis and G. A. Gravvanis. Dependability evaluation using explicit approximate inverse preconditioning. Inter. J. Computational and Numerical Analysis and Applications, 2(1):71–86, 2002.

    Google Scholar 

  30. A. N. Platis, N. Limnios, and M. Le Du. Dependability analysis of systems modeled by non-homogeneous Markov chains. Reliability Engineering and System Safety, 61(3):235–249, 1998.

    Google Scholar 

  31. Y. Saad. Preconditioned Krylov subspace methods for the numerical solution of Markov chains. In W. J. Stewart, eds., Computations with Markov Chains, pp. 49–64. Kluwer Academic, 1995.

  32. S. Seidenari, M. Burroni, G. Dell'Eva, P. Pepe, and B. Belletti. Computerized evaluation of pigmented skin lesion images recorded by a videomicroscope: Comparison between polarizing mode observation and oil/slide mode observation. Skin Res. Technol., 1:187–191, 1995.

    Google Scholar 

  33. D. Slater and G. Healey. The illumination-invariant recognition of 3d objects using local color invariants. IEEE Trans. Pattern Anal. Machine Intell., 18:206–210, 1996.

    Google Scholar 

  34. R. M. Smith, K. S. Trivedi, and A. Ramesh. Performability analysis: Measures, an algorithm and a case study. IEEE Transactions on Computers, C-37(4):406–417, 1988.

    Google Scholar 

  35. W. J. Stewart, Introduction to the Numerical Solution of Markov Chains, Princeton University Press, 1994.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Maglogiannis, I.G., Zafiropoulos, E.P., Platis, A.N. et al. Computing the Success Factors in Consistent Acquisition and Recognition of Objects in Color Digital Images by Explicit Preconditioning. The Journal of Supercomputing 30, 179–198 (2004). https://doi.org/10.1023/B:SUPE.0000040614.03197.e2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:SUPE.0000040614.03197.e2

Navigation