Boundary correction for total variation regularized L1 function with applications to image decomposition and segmentation

Terrence Chen, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The total variation model with L1 norm fidelity term (TV-L 1) has been proposed to serve as an effective cartoontexture image decomposition tool because of its unique scale-dependent decomposition ability. Nevertheless, one of its largely overlooked limitations is its inability to perfectly retain the original contours of the selected patterns when the fidelity term is not sufficiently weighted. In this paper, we propose a boundary correction method to refine the contours of extracted patterns under such circumstances. A scale-driven image segmentation algorithm extended from the boundary correction method is presented as an application. Experimental results demonstrate that our works overcome the drawbacks of existing TV-L1 model and provide an alternative segmentation method.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages316-319
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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    Chen, T., & Huang, T. S. (2006). Boundary correction for total variation regularized L1 function with applications to image decomposition and segmentation. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 316-319). [1699210] (Proceedings - International Conference on Pattern Recognition; Vol. 2). https://doi.org/10.1109/ICPR.2006.340