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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)
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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

Information

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

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References

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