Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost-Reference-Cited by-同舟云学术

Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost

Published:2017-03-22 Issue:2 Volume:59 Page:161-186
ISSN:0924-9907
Container-title:Journal of Mathematical Imaging and Vision
language:en
Short-container-title:J Math Imaging Vis

Author:

Papadakis Nicolas^ORCID,Rabin Julien

Funder

Agence Nationale de la Recherche

Centre National de la Recherche Scientifique

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Geometry and Topology,Computer Vision and Pattern Recognition,Condensed Matter Physics,Modelling and Simulation,Statistics and Probability

Link

http://link.springer.com/article/10.1007/s10851-017-0725-5/fulltext.html

Reference82 articles.

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2. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing, volume 147 of Applied Mathematical Sciences. Springer, New York (2002)

3. Aujol, J.-F., Aubert, G., Blanc-Féraud, L.: Wavelet-based level set evolution for classification of textured images. IEEE Trans. Image Process. 12(12), 1634–1641 (2003)

4. Ayed, I., Chen, H., Punithakumar, K., Ross, I., Li, S.: Graph cut segmentation with a global constraint: recovering region distribution via a bound of the bhattacharyya measure. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’10), pp. 3288–3295 (2010)

5. Berkels, B.: An unconstrained multiphase thresholding approach for image segmentation. In: Scale Space and Variational Methods in Computer Vision (SSVM’09), vol. 5567, pp. 26–37 (2009)

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