A Stochastic-Variational Model for Soft Mumford-Shah Segmentation
In contemporary image and vision analysis, stochastic approaches demonstrate great flexibility in representing and modeling complex phenomena, while variational-PDE methods gain enormous computational advantages over Monte Carlo or other stochastic algorithms. In combination, the two can lead to muc...
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| Format: | Article |
| Language: | English |
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Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/92329 |
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| _version_ | 1849305045166718976 |
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| author | Jianhong (Jackie) Shen |
| author_facet | Jianhong (Jackie) Shen |
| author_sort | Jianhong (Jackie) Shen |
| collection | DOAJ |
| description | In contemporary image and vision analysis, stochastic approaches
demonstrate great flexibility in representing and modeling complex
phenomena, while variational-PDE methods gain enormous
computational advantages over Monte Carlo or other stochastic
algorithms. In combination, the two can lead to much more powerful
novel models and efficient algorithms. In the current work, we
propose a stochastic-variational model for soft (or
fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike
the classical hard Mumford-Shah segmentation, the new
model allows each pixel to belong to each image pattern with some
probability. Soft segmentation could lead to hard segmentation,
and hence is more general. The modeling procedure, mathematical
analysis on the existence of optimal solutions, and computational
implementation of the new model are explored in detail, and
numerical examples of both synthetic and natural images are
presented. |
| format | Article |
| id | doaj-art-415130aec6c047c1a671e963325fb8a5 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-415130aec6c047c1a671e963325fb8a52025-08-20T03:55:33ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962006-01-01200610.1155/IJBI/2006/9232992329A Stochastic-Variational Model for Soft Mumford-Shah SegmentationJianhong (Jackie) Shen0School of Mathematics, Institute of Technology, University of Minnesota, Minneapolis, MN 55455, USAIn contemporary image and vision analysis, stochastic approaches demonstrate great flexibility in representing and modeling complex phenomena, while variational-PDE methods gain enormous computational advantages over Monte Carlo or other stochastic algorithms. In combination, the two can lead to much more powerful novel models and efficient algorithms. In the current work, we propose a stochastic-variational model for soft (or fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike the classical hard Mumford-Shah segmentation, the new model allows each pixel to belong to each image pattern with some probability. Soft segmentation could lead to hard segmentation, and hence is more general. The modeling procedure, mathematical analysis on the existence of optimal solutions, and computational implementation of the new model are explored in detail, and numerical examples of both synthetic and natural images are presented.http://dx.doi.org/10.1155/IJBI/2006/92329 |
| spellingShingle | Jianhong (Jackie) Shen A Stochastic-Variational Model for Soft Mumford-Shah Segmentation International Journal of Biomedical Imaging |
| title | A Stochastic-Variational Model for Soft Mumford-Shah Segmentation |
| title_full | A Stochastic-Variational Model for Soft Mumford-Shah Segmentation |
| title_fullStr | A Stochastic-Variational Model for Soft Mumford-Shah Segmentation |
| title_full_unstemmed | A Stochastic-Variational Model for Soft Mumford-Shah Segmentation |
| title_short | A Stochastic-Variational Model for Soft Mumford-Shah Segmentation |
| title_sort | stochastic variational model for soft mumford shah segmentation |
| url | http://dx.doi.org/10.1155/IJBI/2006/92329 |
| work_keys_str_mv | AT jianhongjackieshen astochasticvariationalmodelforsoftmumfordshahsegmentation AT jianhongjackieshen stochasticvariationalmodelforsoftmumfordshahsegmentation |