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A systemic approach to pattern recognition

Published online by Cambridge University Press:  09 March 2009

H. Emptoz  and
M. Lamure
H. Emptoz
Affiliation:
I.N.S.A., Bâtiment 403, 69621 Villeurbanne Cedex, (France).
M. Lamure
Affiliation:
Bâtiment 101, Université Lyon 1, 43 Boulevard DU 11 Novembre 1918, 69622 Villeurbanne Cedex, (France).
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Summary

We suggest a new pretopological model for pattern recognition which was introduced to study complex economic systems. The model has its origin in the concept of “neighbour”, which is both primitive and fundamental in pattern recognition. Pretopology enables us to develop a perceptive and topological approach for patterns and to see that problems, apparently different, are in fact identical e.g. clustering and recognition, search of skeletons in image processing and search of an informative learning set. It should be noted that the suggeted model is more than a descriptive one.

Keywords

Pattern recognitionNeighbourPretopologyPerceptionClusteringImage analysisDigitalizationMorphology

Information

Type
Article
Information
Robotica , Volume 5 , Issue 2 , April 1987 , pp. 129 - 133
Copyright
Copyright © Cambridge University Press 1987

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References

1.Simon, J.C., “Some current topics in clustering in relation with pattern recognition” 4th IJCPR Kyoto (1978).Google Scholar
2.Koontz, W. and Fukunaga, K., “A non-parametric valley-seeking technique for cluster analysis” IEEE Trans. Comp. C21, 171–178 (1972).Google Scholar
3.Lamure, M., “Espaces abstraits et reconnaissance des formes” Thèse de Doctorat d'Etat (à soutenir, Lyon).Google Scholar
4.Fortin, M., “Sur un algorithme pour l'analyse des données et la reconnaissance des formes” Rev. Stai. Appl. 23, No. 2, 37–45.Google Scholar
5.Bourbaki, N., Elements de Mathématiques-Topologie Générale (Hermann, Paris, (1971)).Google Scholar
6.Auray, J.P., “Elements de pretopologie” Document de Travail No. 18 (Université de Besancon).Google Scholar
7.Brissaud, M., Notes de Compte Rendu à l'Académie des Sciences T.280, Série A, 705–708 and 961–964 (1974).Google Scholar
8.Duru, G., “Contribution à l'étude des structures des systèmes complexes dans les sciences humaines” Thèse de Doctorat d'Etat Es Sciences (Lyon, 1978).Google Scholar
9.Emptoz, H. and Hashom, A., “Fonctions structurantes et classification automatique” Congrès AFCET (Nancy, 1981).Google Scholar
10.Emptoz, H., “Modèle prétopologique pour la reconnaissance des formes – Application à la neurophysiologie” Thèse de Doctorat d'Etat Es Sciences (Lyon, 1983).Google Scholar
11.Matheron, G., Random Sets and Integral Geometry (J. Wiley, New York, 1973).Google Scholar
12.Serra, J., Image Analysis and Mathematical Morphology (Academic Press, New York, 1982).Google Scholar
13.Rosenfeld, A., “Connectivity in digital pictures” J. Assoc. Comp. Mach. 17, No. 1, 146–160.Google Scholar
14.Chassery, J.M., “Représentation discrète, interpretation numerique et description des images: des concepts a l'application” Thése de Doctorat d'Etat Es Sciences (Grenoble, 1981).Google Scholar