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ICPR
2010
IEEE

Robust Figure Extraction on Textured Background: A Game-Theoretic Approach

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Robust Figure Extraction on Textured Background: A Game-Theoretic Approach
Feature-based image matching relies on the assumption that the features contained in the model are distinctive enough. When both model and data present a sizeable amount of clutter, the signal-to-noise ratio falls and the detection becomes more challenging. If such clutter exhibits a coherent structure, as it is the case for textured background, matching becomes even harder. In fact, the large amount of repeatable features extracted from the texture dims the strength of the relatively few interesting points of the object itself. In this paper we introduce a game-theoretic approach that allows to distinguish foreground features from background ones. In addition the same technique can be used to deal with the object matching itself. The whole procedure is validated by applying it to a practical scenario and by comparing it with a standard point-pattern matching technique.
Andrea Albarelli, Emanuele RodolĂ , Alberto Cavall
Added 12 Jan 2011
Updated 12 Jan 2011
Type Journal
Year 2010
Where ICPR
Authors Andrea Albarelli, Emanuele RodolĂ , Alberto Cavallarin, Andrea Torsello
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