Image Segmentation with Multidimensional Refinement Indicators

Abstract : We transpose an optimal control technique to the image segmentation problem. The idea is to consider image segmentation as a parameter estimation problem. The parameter to estimate is the color of the pixels of the image. We use the adaptive parameterization technique which builds iteratively an optimal representation of the parameter into uniform regions that form a partition of the domain, hence corresponding to a segmentation of the image. We minimize an error function during the iterations, and the partition of the image into regions is optimally driven by the gradient of this error. The resulting segmentation algorithm inherits desirable properties from its optimal control origin: soundness, robustness, and flexibility.
Document type :
Journal articles
Inverse Problems in Science and Engineering, Taylor & Francis, 2011, Special Issue: Proceedings of the 5th International Conference on Inverse Problems: Modeling and Simulation, May 24th-29th, 2010, held in Antalya, Turkey, 19 (5), pp.577-597. 〈10.1080/17415977.2011.579609〉
Liste complète des métadonnées

Cited literature [29 references]  Display  Hide  Download

https://hal.inria.fr/inria-00533799
Contributor : Francois Clement <>
Submitted on : Wednesday, May 11, 2011 - 3:20:18 PM
Last modification on : Sunday, October 8, 2017 - 1:06:07 AM
Document(s) archivé(s) le : Saturday, December 3, 2016 - 8:10:59 AM

Files

RR-7446.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Hend Ben Ameur, Guy Chavent, François Clément, Pierre Weis. Image Segmentation with Multidimensional Refinement Indicators. Inverse Problems in Science and Engineering, Taylor & Francis, 2011, Special Issue: Proceedings of the 5th International Conference on Inverse Problems: Modeling and Simulation, May 24th-29th, 2010, held in Antalya, Turkey, 19 (5), pp.577-597. 〈10.1080/17415977.2011.579609〉. 〈inria-00533799v3〉

Share

Metrics

Record views

7357

Document downloads

180