Advertisement

Inverse Quantization of Digital Binary Images for Resolution Conversion

  • Atsushi Imiya
  • Akihiko Ito
  • Yukiko Kenmochi
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)

Abstract

In this paper, we propose an inverse quantization method for planar binary images. The expansion and superresolution of digital binary images involve the same mathematical properties because, for the achievement of these processes, we are required to estimate the original boundary from digitized images which are expressed as a collection of pixels. We first estimate an area through which the original boundary curve should pass through. This area is an orthogonal polygon torus whose two boundary curves are orthogonal polygons. Second, applying curvature flow operation to an orthogonal polygon in this area, we estimate a smooth boundary.

Keywords

Control Point Binary Image Boundary Curve Spline Curve Original Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Isomichi, Y.: Inverse-quantization method for digital signals and images: Point approximation type, Trans. IECE, 63A, 815–821, 1980.Google Scholar
  2. 2.
    Terzopoulos, D.: The computation of visible surface representations, IEEE, Trans, PAMI, 10, 417–438, 1988.zbMATHCrossRefGoogle Scholar
  3. 3.
    Lu, F., and Milios, E.E.: Optimal spline fitting to plane shape, Signal Processing, 37 129–140, 1994.CrossRefGoogle Scholar
  4. 4.
    Wahba, G.: Surface fitting with scattered noisy data on Euclidean D-space and on the sphere, Rocky Mountain Journal of Mathematics, 14, 281–299, 1984.MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Wahba, G., and Johnson, D.R.: Partial splinemodels for the inclusion of tropopause and frontal boundary information in otherwise smooth two-and three-dimensional objective analysis, J. Atmospheric and oceanic technology, 3, 714–725, 1986.CrossRefGoogle Scholar
  6. 6.
    Chen, M.H., and Chin, R.T.: Partial smoothing spline for noisy boundary with corners, IEEE Trans. PAMI, 15, 1208–1216, 1993.CrossRefGoogle Scholar
  7. 7.
    Langridge, D.J.: Curve encoding and the detection of discontinuities, Computer Graphics and Image Processing, 20, 58–71, 1982.CrossRefGoogle Scholar
  8. 8.
    Paglieroni, D., and Jain, A.K.: Control point transformation for shape representation and measurement Computer Graphics and Image Processing, 42, 87–111, 1988.CrossRefGoogle Scholar
  9. 9.
    Medioni, G., and Yasumoto, Y.: Corner detection and curve representation using cubic B-spline, Computer Graphics and Image Processing, 39, 267–278, 1987.zbMATHCrossRefGoogle Scholar
  10. 10.
    I. Daubechies, I., Guskov, I., and Sweldens, W.: Regularity of irregular subdivision, Constructive Approximation, 15, 381–426, 1999.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Daubechies, I., Guskov, I., Schröder, P, and Sweldens, W.: Wavelets on irregular point sets, Phil. Trans. R. Soc. Lond. A, To be published.Google Scholar
  12. 12.
    Boehm, W. and Prautzsch, H.: Numerical Methods, Vieweg, Braunschweig, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Atsushi Imiya
    • 1
  • Akihiko Ito
    • 1
  • Yukiko Kenmochi
    • 2
  1. 1.Institute of Media and Information TechnologyChiba UniversityJapan
  2. 2.School of Information ScienceJAISTJapan

Personalised recommendations