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20231223101634.0 |
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231223s2006 xx ||||| 00| ||eng c |
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Joshi, Dhiraj
|e verfasserin
|4 aut
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| 245 |
1 |
2 |
|a A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models
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| 264 |
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|c 2006
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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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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 08.08.2006
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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 Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images
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|a Journal Article
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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 Li, Jia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, James Z
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 15(2006), 7 vom: 30. Juli, Seite 1871-86
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:15
|g year:2006
|g number:7
|g day:30
|g month:07
|g pages:1871-86
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|d 15
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|h 1871-86
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