Abstract

We propose a polygonal snake segmentation technique adapted to objects that can be composed of several regions with gray-level fluctuations described by a priori unknown probability laws. This approach is based on a histogram equalization and on the minimization of a criterion without parameter to be tuned by the user. We demonstrate the efficiency of this approach, which has low computational cost, on synthetic and real images perturbed by different types of optical noise.

© 2005 Optical Society of America

Information-theory-based snake adapted to multiregion objects with different noise models

Frédéric Galland and Philippe Réfrégier
Opt. Lett. 29(14) 1611-1613 (2004)

Minimal-complexity segmentation with a polygonal snake adapted to different optical noise models

Olivier Ruch and Philippe Réfrégier
Opt. Lett. 26(13) 977-979 (2001)

Information-theory-based snake adapted to inhomogeneous intensity variations

Frédéric Galland and Philippe Réfrégier
Opt. Lett. 32(17) 2514-2516 (2007)

Stochastic complexity integral image based technique for fast video tracking

Jean-François Boulanger, Frédéric Galland, Pascal Martin, and Philippe Réfrégier
Opt. Lett. 33(21) 2521-2523 (2008)

Optical snake-based segmentation processor with a shadow-casting incoherent correlator

E. Hueber, L. Bigué, P. Réfrégier, and P. Ambs
Opt. Lett. 26(23) 1852-1854 (2001)