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Proceedings Paper

Information theory-based snake segmentation adapted to speckled images
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Paper Abstract

Segmentation of coherent active images, which schematically consists in determining the shape of objects present in a scene, is a challenging problem due to the large fluctuations inherent to speckle noise. Snake algorithms have made it possible to improve the segmentation performance of a single object in video images, but they are not well suited to speckled images. Furthermore, they require regularization techniques to obtain smooth contours, which introduces free parameters in the algorithm that must be adjusted by supervised learning. We recently introduced a new technique based on a polygonal description of the contour to be segmented and on the optimization of a statistical criterion. This approach leads to good performance for the segmentation of a target with homogeneous random gray levels but still requires a regularization term. Here, we show that a new technique based on the Minimum Description Length principle makes it possible to efficiently segment an object in a speckled image with a fast algorithm which has no free parameters. This method is thus fully automatic and well suited to speckled images, and we propose to illustrate its capabilities on classical and polarimetric active speckled images.

Paper Details

Date Published: 19 November 2003
PDF: 2 pages
Proc. SPIE 4829, 19th Congress of the International Commission for Optics: Optics for the Quality of Life, (19 November 2003); doi: 10.1117/12.531042
Show Author Affiliations
Olivier Ruch, Thales Optronique SA (France)
Francois Goudail, Ecole Nationale Superieure de Physique de Marseill (France)
Philippe Refregier, Ecole Nationale Superieure de Physique de Marseill (France)

Published in SPIE Proceedings Vol. 4829:
19th Congress of the International Commission for Optics: Optics for the Quality of Life
Giancarlo C. Righini; Anna Consortini, Editor(s)

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