[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

New advances in digital image processing

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Enhancement of noisy image data is a very challenging issue in many research and application areas. In the last few years, non-linear filters, feature extraction, high dynamic range imaging methods based on soft computing models have been shown to be very effective in removing noise without destroying the useful information contained in the image data. In this paper new image processing techniques are introduced in the above mentioned fields, thus contributing to the variety of advantageous possibilities to be applied. The main intentions of the presented algorithms are (1) to improve the quality of the image from the point of view of the aim of the processing, (2) to support the performance, and parallel with it (3) to decrease the complexity of further processing using the results of the image processing phase.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Lim M-H, Gustafson S, Krasnogor N, Ong Y-S (2009) Editorial to the first issue. Memetic Comp 1(1): 1–2

    Article  Google Scholar 

  2. Sattar A, Seguier R (2010) HMOAM: hybrid multi-objective genetic optimization for facial analysis by appearance model. Memetic Comp 2(1): 25–46

    Article  Google Scholar 

  3. Kirstein S, Denecke A, Hasler S, Wersing H, Gross H-M, Körner E (2009) A vision architecture for unconstrained and incremental learning of multiple categories. Memetic Comp 1(4): 291–304

    Article  Google Scholar 

  4. Russo F (1998) Recent advances in fuzzy techniques for image enhancement. IEEE Trans Instrum Meas 47(6): 1428–1434

    Article  Google Scholar 

  5. Xie X, Sudhakar R, Zhuang H (1993) Corner detection by a cost minimization approach. Pattern Recognit 26(8): 1235–1243

    Article  Google Scholar 

  6. Velastin SA, Yin JH, Davies AC, Vicencio-Silva MA, Allsop RE, Penn A (1994) Automated measurement of crowd density and motion using image processing. In: 7th IEE international conference on road traffic monitoring and control, London, UK, 26–28 Apr 1994, pp 127–132

  7. Debevec PE, Taylor CJ, Malik J (1996) Modeling and rendering architecture from photographs a hybrid geometry and image based approach. In: ISIGGRAPH, Aug 1996

  8. Grossmann E, Ortin D, Santos-Victor J (2002) Single and multi-view reconstruction of structured scenes. In: 5th Asian conference on computer vision, Melbourne, Australia, 23–25 Jan 2002

  9. Várkonyi-Kóczy AR (2008) Fuzzy logic supported corner detection. J Intell Fuzzy Syst 19(1): 41–50

    MATH  Google Scholar 

  10. Harris C, Stephens M (1988) A combined corner and edge detector. In: 4th Alvey vision conference, pp 189–192

  11. Förstner W (1986) A feature based correspondence algorithm for image matching. Int Arch Photogramm Remote Sens 26: 150–166

    Google Scholar 

  12. Smith SM, Brady M (1997) SUSAN—a new approach to low level image processing. Int J Comput Vis 23(1): 45–78

    Article  Google Scholar 

  13. Russo F (1996) Fuzzy filtering of noisy sensor data. In: IEEE instrumentation and measurement technology conference, Brussels, Belgium, 4–6 June 1996, pp 1281–1285

  14. Catté F, Lions P-L, Morel J-M, Coll T (1992) Image selective smoothing and edge detection by nonlinear diffusion. SIAM J Numer Anal 32: 1895–1909

    Article  Google Scholar 

  15. Felsberg M (2002) Low-level image processing with the structure multivector. PhD dissertation, Institute of Computer Science and Applied Mathematics, Christian-Albrechts-University of Kiel, TR no. 0203

  16. Várkonyi-Kóczy AR, Rövid A (2005) Soft computing based point corresponding matching for automatic 3D reconstruction. Acta Polytech Hung (Special Issue on Computational Intelligence) 2(1): 33–44

    Google Scholar 

  17. Long F, Zhang , H , Dagan Feng D (2003) Fundamentals of content based image retrieval, Chap 1. In: Feng D, Siu WC, Zhang HJ (eds) Multimedia information retrieval and management. Springer, Berlin

    Google Scholar 

  18. Assfalg J, Bimbo AD, Pala P (2000) Using multiple examples for content-based retrieval. In: Proceedings of the multimedia and expo, ICME, vol. 1, pp 335–338

  19. Lu C, Cao Y, Mumford D (2002) Surface evolution under curvature flows. Special issue on Partial Differential Equations (PDE’s) in Image Processing Computer Vision, and Computer Graphics, p 19

  20. Gray A (1997) “The Gaussian and mean curvatures” and “surfaces of constant Gaussian curvature,” §16.5 and chap. 21 in modern differential geometry of curves and surfaces with mathematica, 2nd edn. CRC Press, Boca Raton, pp 373–380, 481–500

  21. Várkonyi-Kóczy AR, Rövid A (2008) Fuzzy logic supported primary edge extraction in image understanding. In: CD-ROM Proceedings of the 17th IEEE international conference on fuzzy systems, FUZZ-IEEE’2008, Hong Kong, China, 1–6 June 2008

  22. Adelson EH, Pentland AP (1996) The perception of shading and reflectance. In: Knill D, Richards W (eds) Perception as Bayesian inference. Cambridge University Press, New York, pp 409–423

    Google Scholar 

  23. Adelson EH (2000) Lightness perception and lightness illusions. In: The cognitive neurosciences, 2nd edn. MIT Press, Cambridge, pp 339–351

  24. Shin H, Reyes NH (2009) Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth. Memetic Comp. doi:10.1007/s12293-009-0025-8

  25. Palmer S (1999) Vision science: photons to phenomenology. In: Surface-based color processing, chap 3.3. The MIT Press

  26. Scheel A, Stamminger M, Seidel H-P (2000) Tone reproduction for interactive walkthroughs. In: EUROGRAPHICS’2000, vol 19, no 3, Saarbrücken, Germany

  27. Kawahito S (2005) An ultimate dynamic range imaging device—from star light to sun light. In: Proceedings of the Inter-Academia’2005, Wuppertal, Germany, vol. 1, pp 105–113, 19–22 Sept 2005

  28. Reinhard E, Stark M, Shirley P, Ferwerda PJ (2002) Photographic tone reproduction for digital images. In: Proceedings of the 29th annual conference on computer graphics and interactive techniques, San Antonio, Texas, pp 267–276

  29. Várkonyi-Kóczy AR, Rövid A, Várlaki P (2006) Fuzzy based brightness compensation for high dynamic range images. Int J Adv Comput Intell Intell Inf (JACIII) 10(4): 549–554

    Google Scholar 

  30. Várkonyi-Kóczy AR, Rövid A (2007) High dynamic range image reproduction methods. IEEE Trans Instrum Meas 56(4): 1465–1472

    Article  Google Scholar 

  31. Ukovich A, Impoco G, Reamponi G (2005) A tool based on the co-occurance matrix to measure the performance of dynamic range reduction algorithms. In: Proceedings of the IEEE international workshop on imaging systems and techniques, IST’2005, pp 36–41, 13 May 13 2005

  32. Gilchrist A, Kossyfidis C, Bonato F, Agostini T, Cataliotti J, Li X, Spehar B, Annan V, Economou E (1999) An anchoring theory of lightness perception. Psychol Rev 106(4): 795–834

    Article  Google Scholar 

  33. Rudd ME, Zemach IK (2003) The highest luminance anchoring rule in lightness perception. J Vis 3(9): 56a

    Article  Google Scholar 

  34. Theiler J, Gisler G (1997) A contiguity-enhanced k-means clustering algorithm for unsupervised multispectral image segmentation. In: Proc SPIE 3159:108–118

  35. Krawczyk G, Myszkowski K, Seidel HP (2005) Lightness perception in tone reproduction for high dynamic range images. In: Proceedings of EUROGRAPHICS ’05 (Computer Graphics Forum, vol 24)

  36. Ward G (1994) A contrast-based scalefactor for luminance display. In: Heckbert P (eds) Graphics gems IV. Academic Press, Boston, pp 415–421

    Google Scholar 

  37. Várkonyi-Kóczy AR, Rövid A, Hashimoto T (2007) HDR colored information enhancement based on fuzzy image synthesization. In: Proceedings of the 5th IEEE international symposium on intelligent signal processing, WISP’2007. Alcala de Henares, Spain, pp 187–192, 3–5 Oct 2007

  38. Várkonyi-Kóczy AR (2009) Improved fuzzy logic supported HDR colored information enhancement. In: CD-ROM proceedings of the 2009 IEEE international instrumentation and measurement technology conference, I2MTC’2009, Singapore, 5–7 May 2009

  39. Lou Haskell’s photos. http://www.easyhdr.com

Download references

Author information

Authors and Affiliations

  1. Institute of Mechatronics and Vehicle Engineering, Óbuda University, Népszínház u. 8, Budapest, 1081, Hungary

    Annamária R. Várkonyi-Kóczy

Authors
  1. Annamária R. Várkonyi-Kóczy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annamária R. Várkonyi-Kóczy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Várkonyi-Kóczy, A.R. New advances in digital image processing. Memetic Comp. 2, 283–304 (2010). https://doi.org/10.1007/s12293-010-0046-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-010-0046-3

Keywords

Search

Navigation