Skip to main content
Log in

Topographic Maps and Local Contrast Changes in Natural Images

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

We call “natural” image any photograph of an outdoor or indoor scene taken by a standard camera. We discuss the physical generation process of natural images as a combination of occlusions, transparencies and contrast changes. This description fits to the phenomenological description of Gaetano Kanizsa according to which visual perception tends to remain stable with respect to these basic operations. We define a contrast invariant presentation of the digital image, the topographic map, where the subjacent occlusion-transparency structure is put into evidence by the interplay of level lines. We prove that each topographic map represents a class of images invariant with respect to local contrast changes. Several visualization strategies of the topographic map are proposed and implemented and mathematical arguments are developed to establish stability properties of the topographic map under digitization.

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.

Similar content being viewed by others

References

  • Aliprantis, Ch.D. and Border, K.C. 1994. Infinite Dimensional Analysis. Springer Verlag.

  • Alison Noble, J. 1992. Finding half boundaries and junctions in images. Image and Vision Computing, 10(4).

  • Alvarez, L., Guichard, F., Lions, P.L., and Morel, J.M. 1993. Axioms and fundamental equations of image processing. Arch. Rational Mechanics and Anal. 16(IX):200–257.

    Google Scholar 

  • Alvarez, L. and Morales, F. 1994. Affine morphological multiscale analysis of corners and multiple junctions. International Journal of Computer Vision, 25(2):95–108.

    Google Scholar 

  • Alvarez, L. and Morel, J.M. 1994. Formalization and computational aspects of image analysis. Acta Numerica, pp. 1–59.

  • Ambrosio, L., Caselles, V., Masnou, S., and Morel, J.M. 1999. The Connected Components of Sets of Finite Perimeter and Applications to Image Processing. Preprint SNS, Pisa, Italy.

  • Ballester, C., Cubero-Castan, E., Gonzalez, M., and Morel, J.M. 1998. Contrast Invariant Image Intersection, preprint.

  • Beymer, D.J. 1991. Finding junctions using the image gradient. Computer Vision and Pattern Recognition, Lahaiana, Maui, Hawai, pp. 720–721.

  • Brunnström, K., Linderberg, T., and Eklundh, J.O. 1992. Active detection and classification of junctions by foveation with a head-eye system guided by the scale-space primal sketch. Technical Report ISRN KTH/NA/P-91/31, CVAP, Royal Institute of Technology.

  • Canny, J.F. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:769–798.

    Google Scholar 

  • Caselles, V., Catté, F., Coll, B., and Dibos, F. 1993. A Geometric Model for Edge Detection. Numerische Mathematik, 66:1–31.

    Google Scholar 

  • Caselles, V., Coll, B., and Morel, J.M. 1995. A Kanizsa programme. Preprint CEREMADE 9539, Univ. Paris-Dauphine.

  • Caselles, V., Coll, B., and Morel, J.M. 1997. Scale-space versus topographic map for natural images. In Scale-space '97. Proc. First Conference on Scale Space, Utrecht.

  • Caselles, V., Lisani, J.L., Morel, J.M., and Sapiro, G. 1997. Shape preserving local histogram modification. Technical Report HPL–97–58.

  • Caselles, V., Morel, J.M., and Sbert, C. 1998. An axiomatic approach to image interpolation. Special Issue on PDE's, Geometry Driven Diffusion and Image Processing. IEEE Transactions on Image Processing, 7(3):376–386.

    Google Scholar 

  • Castleman, K.R. 1996. Digital Image Processing. Prentice Hall.

  • Choquet, G. 1966. Topology, Academic Press: New York.

    Google Scholar 

  • Deriche, R. and Blaszka, T. 1993. Recovering and characterizing image features using and efficient model based approach. Proc. CVPR, New York, pp. 530–535.

  • Deriche, R. and Giraudon, G. 1993. A computational approach for corner and vertex detection. International Journal of Computer Vision, 10(2):101–124.

    Google Scholar 

  • Eatwell, J., Milgate, M., and Newman, P. (Eds.). 1994. The New Palgrave, a Dictionnary of Economics, 4 volumes. The Macmillan Press Limited.

  • Evans, L.C. and Gariepy, R. 1992. Lectures Notes on Measure Theory and Fine Properties of Functions. CRC Press.

  • Fitch, J.P., Coyle, E.C., and Gallagher, N.C. 1985. Threshold decomposition of multi-dimensional ranked order operations. IEEE Trans. on Circuits and Systems, 5:445–450.

    Google Scholar 

  • Florack, L.M.J. 1993. The syntactical structure of scalar images. Ph.D. Thesis, University of Utrecht, The Netherlands.

    Google Scholar 

  • Froment, J. et al. 1997. MegaWave, Image Processing Environment, Ceremade, Université Paris-Dauphine.

  • Froment, J., A Functional Analysis Model for Natural Images Permitting Structured Compression. Preprint 9833, CMCA, ENS Cachan, France.

  • Fuchs, W. 1923. Experimentelle Untersuchungen ueber das simultane Hintereinandersehen auf der selben Sehrichtung. Zeitschrift fuer Psychologie, 91:154–253.

    Google Scholar 

  • Haar Romeny, B.M. ter. 1994. Geometry-Driven Diffusion in Computer Vision. Kluwer Academic Publications.

  • Haar Romeny, B.M. ter, Florack, L.M.J., Koendering, J.J., and Viergever, M.A. 1991. Invariant third-order properties of isophotes: T-junction detection. In Proc. Scandinavian Conference on Image Analysis.

  • Haralick, R.M. and Shapiro, L.G. 1992. Computer and Robot Vision, I. Addison-Wesley.

  • Hecht, E. and Zajac, A. 1986. Optica. Addison Wesley Iberoamericana.

  • Heitger, F. and von der Heydt, R. 1993. A computational model of neural contour processing: Figure-ground segretation and illusory contours. In 4th Proc. International Conference on Computer Vision, Berlin, Germany. IEEE Computer Society Press, pp. 32–40.

  • Illueca, C. 1995. Influence of contrast of the recognition of defocused letters: A geometrical model. Vision Research.

  • Iverson, L.A. and Zucker, S.W. 1995. Logical/linear operators for image curves. IEEE Trans. Pattern Anal. Machine Intell. 17(10): 982–996.

    Google Scholar 

  • Julesz, B. 1981. Textons, the elements of texture perception, and their interactions. Nature, 290(3).

  • Julesz, B. 1986. Texton gradients: The texton theory revisited. Biological Cybernetics, 54:245–251.

    Google Scholar 

  • Kanizsa, G. 1979. Organization in Vision. NY, Praeger.

    Google Scholar 

  • Kanizsa, G. 1991. Vedere e pensare. Il Mulino, Bologna.

    Google Scholar 

  • Kimia, B.B., Tannenbaum, A., and Zucker, S.W. 1992. On the evolution of curves via a function of curvature, 1: The classical case. J. of Math. Analysis and Applications, 163(2).

  • Koenderink, J.J. 1984. The structure of images. Biological Cybernetics, 50:363–370.

    Google Scholar 

  • Leclerc, Y.G. and Zucker, S.W. 1987. The local structure of Image discontinuities in one dimension. IEEE Trans. Pattern Anal. Machine Intell., 9:4.

    Google Scholar 

  • Lindeberg, T. 1994. Junction detection with automatic selection of detection scales and localization scales. In Proc. First Int. Conf. on Image Processing, Austin, Texas, vol. 1, pp. 924–928.

    Google Scholar 

  • Malik, J. 1987. Interpreting line drawings of curved objects. Int. J. Comp. Vision, 1.

  • Malladi, R., Sethian J., and Vemuri, B.C. 1995. Shape modelling with front propagation: A level set approach. IEEE Trans. on Pattern Anal. Machine Intell., 17(2):158–175.

    Google Scholar 

  • Marr, D. 1981. Vision, Freeman and Co.

  • Masnou, S. and Morel, J.M. 1997. Restauration d'images et filtres de Yaroslarsky. In Proc. Workshop GRETSI 97, Grenoble, France.

  • Masnou, S. and Morel, J.M. 1998a. Level lines based disocclusion. In Proc. Int. Conf. Image Processing, ICIP'98, Chicago, USA, vol. III, pp. 259–263.

    Google Scholar 

  • Masnou, S. and Morel, J.M. 1998b. Image restoration involving connectedness. In Proc. of Workshop “Digital Image Processing”, Vienna, Austria 1997, SPIE, vol. 3346.

  • Matheron, G. 1975. Random Sets and Integral Geometry, JohnWiley, NY.

    Google Scholar 

  • Monasse, P. and Guichard, F. 1998. Fast computation of a contrast invariant image representation. Cahiers de l'ENS Cachan, 9815.

  • Morel, J.M. and Solimini, S. 1994. Variational Methods in Image Processing. Birkhäuser.

  • Nitzberg, M. and Mumford, D. 1990. The 2.1 sketch. In Proc. of the Third International Conference on Computer Vision, Osaka.

  • Nitzberg, M., Mumford, D., and Shiota, T. 1993. Filtering, Segmentation and Depth. Lecture Notes in Computer Science, 662, Springer-Verlag.

  • Osher, S. and Sethian, J. 1988. Fronts propagating with curvature dependent speed: Algorithms based on the Hamilton-Jacobi formulation. J. Comp. Physics, 79:12–49.

    Google Scholar 

  • Rudin, L.I. 1987. Images, numerical analysis of singularities and shock filters. Ph.D. Dissertation, Caltech, Pasadena, CA, n5250:TR.

  • Rudin, L.I., Osher, S., and Fatemi, E. 1992. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–269.

    Google Scholar 

  • Sapiro, G. and Tannenbaum, A. 1993. Affine invariant scale space. The International Journal of Computer Vision, 11(1):25–44.

    Google Scholar 

  • Sapiro, G. and Tannenbaum, A. 1994. On affine plane curve evolution. Journal of Functional Analysis, 119(1):79–120.

    Google Scholar 

  • Serra, J. 1982. Image Analysis and Mathematical Morphology. Academic Press.

  • Vincent, L. 1993. Grayscale area openings and closings, their efficient implementation and applications. In FirstWorkshop on Mathematical Morphology and its Applications to Signal Processing, J. Serra and Ph. Salembier (Eds.), Barcelona, Spain, pp. 22–27.

  • Wertheimer, M. 1923. Untersuchungen zur Lehre der Gestalt, II. Psychologische Forschung, 4:301–350.

    Google Scholar 

  • Witkin, A.P. 1983. Scale-space filtering. In Proc. of IJCAI, Karlsruhe, pp. 1019–1021.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Caselles, V., Coll, B. & Morel, JM. Topographic Maps and Local Contrast Changes in Natural Images. International Journal of Computer Vision 33, 5–27 (1999). https://doi.org/10.1023/A:1008144113494

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008144113494

Navigation