Shape recognition via an a contrario model for size functions

Abstract : Shape recognition methods are often based on feature comparison. When features are of different natures, combining the value of distances or (dis-)similarity measures is not easy since each feature has its own amount of variability. Statistical models are therefore needed. This article proposes a statistical method, namely an a contrario method, to merge features derived from several families of size functions. This merging is usually achieved through a touchy normalizing of the distances. The proposed model consists in building a probability measure. It leads to a global shape recognition method dedicated to perceptual similarities.
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Communication dans un congrès
3rd International Conference on Image Analysis and Recognition - ICIAR 2006, Sep 2006, Povoa de Varzim, Portugal. Springer Verlag, 4142, pp.410-421, 2006, Lecture Notes in Computer Science. 〈10.1007/11867661_37〉
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https://hal.inria.fr/inria-00104021
Contributeur : Frédéric Sur <>
Soumis le : jeudi 5 octobre 2006 - 16:20:52
Dernière modification le : jeudi 11 janvier 2018 - 06:20:14

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Andrea Cerri, Daniela Giorgi, Pablo Musé, Frédéric Sur, Federico Tomassini. Shape recognition via an a contrario model for size functions. 3rd International Conference on Image Analysis and Recognition - ICIAR 2006, Sep 2006, Povoa de Varzim, Portugal. Springer Verlag, 4142, pp.410-421, 2006, Lecture Notes in Computer Science. 〈10.1007/11867661_37〉. 〈inria-00104021〉

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