Minimal stochastic complexity snake-based technique adapted to an unknown noise model
Optics Letters, Vol. 30, Issue 17, pp. 2239-2241 (2005)
http://dx.doi.org/10.1364/OL.30.002239
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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
OCIS Codes
(100.5010) Image processing : Pattern recognition
(110.2970) Imaging systems : Image detection systems
(110.4280) Imaging systems : Noise in imaging systems
Citation
Frédéric Galland and Philippe Réfrégier, "Minimal stochastic complexity snake-based technique adapted to an unknown noise model," Opt. Lett. 30, 2239-2241 (2005)
http://www.opticsinfobase.org/ol/abstract.cfm?URI=ol-30-17-2239
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