dc.contributor.author | Yuan, Jing | eng |
dc.contributor.author | Bae, Egil | eng |
dc.contributor.author | Tai, Xue-Cheng | eng |
dc.date.accessioned | 2011-09-19T11:15:51Z | |
dc.date.available | 2011-09-19T11:15:51Z | |
dc.date.issued | 2010 | eng |
dc.Published | In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2217-2224. | en |
dc.identifier.isbn | 978-1-4244-6984-0 (print version) | en_US |
dc.identifier.uri | https://hdl.handle.net/1956/5020 | |
dc.description | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (An extended journal version). | en |
dc.description.abstract | We propose and investigate novel max-flow models in the spatially continuous setting, with or without supervised constraints, under a comparative study of graph based max-flow / min-cut. We show that the continuous max-flow models correspond to their respective continuous min-cut models as primal and dual problems, and the continuous min-cut formulation without supervision constraints regards the well-known Chan-Esedoglu-Nikolova model [15] as a special case. In this respect, basic conceptions and terminologies applied by discrete max-flow / mincut are revisited under a new variational perspective. We prove that the associated nonconvex partitioning problems, unsupervised or supervised, can be solved globally and exactly via the proposed convex continuous max-flow and min-cut models. Moreover, we derive novel fast max-flow based algorithms whose convergence can be guaranteed by standard optimization theories. Experiments on image segmentation, both unsupervised and supervised, show that our continuous max-flow based algorithms outperform previous approaches in terms of efficiency and accuracy. | en_US |
dc.language.iso | eng | eng |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | <a href="http://hdl.handle.net/1956/5017" target="blank">Efficient global minimization methods for variational problems in imaging and vision</a> | en_US |
dc.subject | Image processing and segmentation | eng |
dc.subject | Continuous max-flow/min-cut | eng |
dc.subject | Optimization | eng |
dc.title | A Study on Continuous Max-Flow and Min-Cut Approaches | en_US |
dc.type | Chapter | |
dc.type | Peer reviewed | |
dc.description.version | updatedVersion | en_US |
dc.rights.holder | Copyright IEEE. All rights reserved. | en_US |
dc.identifier.doi | https://doi.org/10.1109/cvpr.2010.5539903 | |
dc.subject.nsi | VDP::Mathematics and natural science: 400::Mathematics: 410 | en_US |
dc.subject.nsi | VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429 | en_US |