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Improve the Nonparametric Image Segmentation with Narrowband Levelset and Fast Gauss Transformation
School of Information Science and Engineering, Yunnan University, Kunming, China
School of Information Science and Engineering, Yunnan University, Kunming, China
School of Information Science and Engineering, Yunnan University, Kunming, China
School of Information Science and Engineering, Yunnan University, Kunming, China
School of Information Science and Engineering, Yunnan University, Kunming, China
- 1 School of Information Science and Engineering, Yunnan University, Kunming, China
- 2 School of Information Science and Engineering, Yunnan University, Kunming, China
- 3 School of Information Science and Engineering, Yunnan University, Kunming, China
- 4 School of Information Science and Engineering, Yunnan University, Kunming, China
- 5 School of Information Science and Engineering, Yunnan University, Kunming, China
Applied Mathematics·Volume 03 (2012)·Pages 1836–1841·Published 27 November 2012·DOI10.4236/am.2012.331249
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Abstract
Nonparametric method based on the mutual information is an efficient technique for the image segmentation. In this method, the image is divided into the internal and external labeled regions, and the segmentation problem constrained by the total length of the region boundaries will be changed into the maximization of the mutual information between the region labels and the image pixel intensities. The maximization problem can be solved by deriving the associated gradient flows and the curve evolutions. One of the advantages for this method does not need to choose the segmentation parameter; another is not sensitive to the noise. By employing the narrowband levelset and Fast Gauss Transformation, the computation time is reduced clearly and the algorithm efficiency is greatly improved.
KeywordsNonparametric Image SegmentationMutual InformationNarrowband LevelsetFast Gauss Transformation
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