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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2006

Minimal stochastic complexity image partitioning with unknown noise model

Frédéric Galland

Résumé

We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the grey level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image. We demonstrate that efficient homogeneous image partitioning can be obtained when no parametric model of the probability laws of the grey levels is used and that this approach leads to a criterion without parameter to be tuned by the user. The efficiency of this technique is compared to a statistical parametric technique on a synthetic image and is compared to a standard unsupervised segmentation method on real optical images.
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Dates et versions

hal-00079459 , version 1 (12-06-2006)

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  • HAL Id : hal-00079459 , version 1

Citer

Guillaume Delyon, Frédéric Galland, Philippe Réfrégier. Minimal stochastic complexity image partitioning with unknown noise model. IEEE Transactions on Image Processing, 2006, 15 (10), pp.3207-3212. ⟨hal-00079459⟩
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