New region force for variational models in image segmentation and high dimensional data clustering
Open Access
- 1 January 2018
- journal article
- Published by International Press of Boston in Annals of Mathematical Sciences and Applications
- Vol. 3 (1) , 255-286
Abstract
International Press of Boston - publishers of scholarly mathematical and scientific journals and booksKeywords
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This publication has 28 references indexed in Scilit:
- An Effective Region Force for Some Variational Models for Learning and ClusteringJournal of Scientific Computing, 2017
- Global Binary Optimization on Graphs for Classification of High-Dimensional DataJournal of Mathematical Imaging and Vision, 2015
- Efficient Global Minimization Methods for Image Segmentation Models with Four RegionsJournal of Mathematical Imaging and Vision, 2014
- Multi-class Transductive Learning Based on ℓ 1 Relaxations of Cheeger Cut and Mumford-Shah-Potts ModelJournal of Mathematical Imaging and Vision, 2013
- A spatially continuous max-flow and min-cut framework for binary labeling problemsNumerische Mathematik, 2013
- On the Convergence of Primal–Dual Hybrid Gradient Algorithms for Total Variation Image RestorationJournal of Mathematical Imaging and Vision, 2012
- A First-Order Primal-Dual Algorithm for Convex Problems with Applications to ImagingJournal of Mathematical Imaging and Vision, 2010
- What Energy Functions Can Be Minimized via Graph Cuts?2002
- Optimal approximations by piecewise smooth functions and associated variational problemsCommunications on Pure and Applied Mathematics, 1989
- Snakes: Active contour modelsInternational Journal of Computer Vision, 1988