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|a eng
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| 100 |
1 |
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|a Kim, Junmo
|e verfasserin
|4 aut
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| 245 |
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|a A nonparametric statistical method for image segmentation using information theory and curve evolution
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|c 2005
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 15.11.2005
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training
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| 650 |
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|a Comparative Study
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| 650 |
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|a Evaluation Study
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Fisher, John W
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yezzi, Anthony
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cetin, Müjdat
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Willsky, Alan S
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 14(2005), 10 vom: 01. Okt., Seite 1486-502
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|x 1057-7149
|7 nnns
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|g volume:14
|g year:2005
|g number:10
|g day:01
|g month:10
|g pages:1486-502
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