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231223s2007 xx ||||| 00| ||eng c |
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|e rakwb
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|a eng
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| 100 |
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|a Pyun, Kyungsuk
|e verfasserin
|4 aut
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|a Image segmentation using hidden Markov Gauss mixture models
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|c 2007
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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
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 26.07.2007
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|a Date Revised 26.10.2019
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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 Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM
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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 Lim, Johan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Won, Chee Sun
|e verfasserin
|4 aut
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| 700 |
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|a Gray, Robert M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 16(2007), 7 vom: 28. Juli, Seite 1902-11
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|x 1941-0042
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|g year:2007
|g number:7
|g day:28
|g month:07
|g pages:1902-11
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