Abstract
This paper presents a global system for the fusion of images segmented by various methods and interpreted by a fuzzy classifier. A set of complementary segmentation operators is applied to the image. Each region of the segmented images is interpreted by the fuzzy classifier, through membership degrees to classes. The fuzzy classifier builds the classes automatically from examples, even in the case of complex data sets. Interpreted images are then merged by a fusion operator from the fuzzy set theory. Several fusion operators are compared. They trust more high membership degrees to classes, which are considered as reliability degrees. The fusion of the interpreted images improves the segmentation, and gives solutions to segmentation and interpretation evaluation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Cocquerez J-P, Philipp S (editors). Analyse d'images: filtrage et segmentation. Masson, 1995
Pavlidis T, Liow, Y-T. Integration region growing and edge detection. IEEE Trans PAMI 1990; 12(3): 225–233
Gambotto J-P. A new approach to combining region growing and edge detection. Pattern Recognition Letters 1993; 14: 869–875
Kara-Falah R, Bolon Ph, Cocquerez J-P. A region-region and region-edge cooperative approach of image segmentation. Proceedings of IEEE ICIP-94, vol 3, Austin, TX, November 1994, pp 470–474
Charroux B, Philipp S, Cocquerez J-P. Image analysis: segmentation operator cooperation led by the interpretation. Proceedings of ICIP 96, vol. III, Lausanne, Switzerland, September 1996, pp 939–942
Chu C-C, Aggarwal J-K. The integration of image segmentation maps using region and edge information. IEEE Trans PAMI 1993; 15(2): 1241–1252
Kara-Falah R. Approche coopérative pour la segmentation d'images naturelles. PhD Thesis, University of Savoie, France, 1995
Anderson TW. An Introduction to Multivariate Statistical Analysis. Wiley, New York, 1958
Fukunaga K. Introduction to Statistical Pattern Recognition. Academic Press, New York 1972
Ruspini EH. A new approach to clustering. Information and Control 1969; 15(1): 22–32
Ruspini EH. Numerical methods for fuzzy clustering. Information Sciences 1970; 2(3): 319–350
Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, 1981
Hajjami HE. Application de la théorie des sous-ensembles flous pour le développement d'un algorithme séquentiel non supervisé et non paramétrique pour le suivi en temps réel de l'évolution de l'état d'une structure soumise à des sollicitations extérieures. PhD thesis, UTC, France, 1991
Grenier D. Méthode de détection d'évolution. Application à l'instrumentation nucléaire. PhD thesis, UTC, France, 1984
Charroux B. Analyse d'images: coopération d'opérateurs de segmentation guidée par l'interprétation. PhD thesis. University Paris XI Orsay, France, 1996
Fukunaga K, Flick TE. An optimal global nearest neighbor metric. IEEE Trans PAMI 1984; 6(3): 314–318
Cover JM, Hart PE. Nearest neighbour pattern classification. IEEE Trans Info Theory 1967; 13(1): 21–22
Dudani SA. The distance-weighted k-nearest neighbor rule. IEEE Trans SMC 1976: 325–327
Brown TA, Koplowitz J. The weighted nearest neighbor rule for class dependent sample sizes. IEEE Trans Information Theory 1979; 25(5): 617–619
Short RD, Fukunaga K. A new nearest neighbor distance measure. Proceedings of ICPR, Miami, FL, December 1980, pp 81–86.
Dubuisson B. Diagnostic et Reconaissance des Formes. Hermes, 1990
Cocquerez J-P, Philipp S, Gaussier P. Comparison between systems of image interpretation in neuro-computation in remote sensing data analysis 86–96. Springer-Verlag, Berlin, 1997
Rosenfeld A, Hummel RA, Zucker SW. Scene labeling by relaxation operation. IEEE Trans SMC 1976; 6(6): 420–433
Cocquerez J-P, Gaussier P, Philipp S. Un système d'interprétation mixte réseaux de neurones/système expert. Traitement du Signal 1992; 9(5): 421–439
Charroux B, Philipp S. Interpretation of aerial images based on potential functions. Proceedings of 9th SCIA, Uppsala, Sweden, 1995, pp 671–678
Deriche R. Using Canny's criteria to derive a recursively implemented optimal edge detector. Int J Computer Vision 1987; 1(2): 167–187
Shen J, Castan S. An optimal linear operator for step edge detection. CVGIP 1992; 54: 112–113.
Canny J-F. A computational approach to edge detection. IEEE Trans PAMI 1986; 8: 679–698
Cocquerez J-P, Devars J. Detection du contour dans les images aériennes: nouveaux opérateurs. Traitement du Signal 1985; 2(1): 45–64
Deriche R, Cocquerez J-P, Almouzni G. An efficient method to build early image description. Proceedings of 9th ICPR, Rome, Italy, 1988
Chassery J-M, Garbay C. An iterative segmentation method based on a contextual color and shape criterion. IEEE Trans PAMI 1984; 6: 794–800
Bloch I, Maître H. Fusion de données en traitements des images. Traitement du Signal 1994; 11(6): 435–446
Bloch I. Information combination operators for data fusion: a comparative review with classification. IEEE Trans on SMC 1995; 26(1): 52–67
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huet, F., Philipp, S. Fusion of images interpreted by a new fuzzy classifier. Pattern Analysis & Applic 1, 231–247 (1998). https://doi.org/10.1007/BF01234770
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF01234770