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Local Statistic Based Region Segmentation with Automatic Scale Selection

Jérome Piovano 1 Théodore Papadopoulo 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Recently, new segmentation models based on local information have emerged. They combine local statistics of the regions along the contour (inside and outside) to drive the segmentation procedure. Since they are based on local decisions, these models are more robust to local variations of the regions of interest (contrast, noise, blur, . . . ). They nonetheless also introduce some new difficulties which are inherent to the fact of basing a global property (the segmentation) on pure local decisions. This papers explores some of those difficulties and proposes some possible corrections. Results on both 2D and 3D data are compared to those obtained without these corrections.
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Jérome Piovano, Théodore Papadopoulo. Local Statistic Based Region Segmentation with Automatic Scale Selection. European Conference on Computer Vision 2008, Oct 2008, Marseille, France. pp.486--499, ⟨10.1007/978-3-540-88688-4_36⟩. ⟨inria-00423331⟩

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