Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-23T13:42:29.097Z Has data issue: false hasContentIssue false
  • English
  • Français

New methods in artificial vision by using entropies of deterministic functions*

Published online by Cambridge University Press:  09 March 2009

Guy Jumarie
Guy Jumarie
Affiliation:
Dept. of Mathematics and Computer Science, Université du Québec à Montréal, P.O. Box 8888, St A, Montréal, QUE, H3C3P8 (Canada)
Get access Rights & Permissions [Opens in a new window]

Summary

The purpose of this paper is to show how one can use entropies of deterministic functions (as previously defined by the author) in order to analyze some questions related to machine vision. The main advantage of this approach is that it provides information theoretic methods for solving problems which basically do not refer to probability distributions. After a short qualitative background on deterministic functions, one applies this theory to edge finding, image segmentation, transfer of information defined by brightness functions, and image processing. Some more theoretical details on the entropies of deterministic functions are given in the appendix.

Keywords

Artificial visionEntropyInformationDeterministic functionsImage processing
Type
Article
Information
Robotica , Volume 10 , Issue 2 , March 1992 , pp. 105 - 112
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Jumarte, G., “Systems, Catastrophe, Chaos, Synergetics. A Unified Approach via Information of Deterministic Functions” J. Systems Analysis, Modelling, Simulation 6, No 5, 323–362 (1989).Google Scholar
2.Jumarie, G., Relative Information. Theories and Applications (Springer Verlag, Berlin, 1990).CrossRefGoogle Scholar
3.Jumarie, G., “Quantum Entropy of Non Probabilistic Matrices” J. Mathematical Physics (to appear, 1992).Google Scholar
4.Marr, D., “Early Processing of Visual Information” Philosophical Trans, of the Royal Society of London, B, 275, 483–524 (1976).Google ScholarPubMed
5.Marr, D. and Ullman, S., “Directional Selectivity and its Use in Early Visual Processing” Proc. of the Royal Society of London B. 211, 151–180 (1981).Google ScholarPubMed
6.Horn, B.K.P., Robot Vision (The MIT Press, Cambridge, Massachusetts, 1986).Google Scholar
7.Jaynes, E.T., “Information Theory and Statistical Mechan ics” Physical Review 106, 620–630 (1957); 108, 171–190 (1957).CrossRefGoogle Scholar
8.Pratt, W., Digital Image Processing (John Wiley, New York, 1978).Google Scholar
9.Rosenfeld, A. and Kak, A.C., Digital Picture Processing 1 & 2, 2nd Edit. (Academic Press, New York, 1982).Google Scholar
10.Jumarie, G., “On the Use of Deformation Matrices in Artificial Vision Systems Robotica 6, No. 1, 13–21 (1988).CrossRefGoogle Scholar
11.Haken, H., Information and Self-Organization (Springer Verlag, Berlin, 1988).CrossRefGoogle Scholar