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20250205202537.0 |
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231223s2004 xx ||||| 00| ||eng c |
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|a pubmed25n0502.xml
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|a (DE-627)NLM150510268
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|a (NLM)15376586
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Freedman, Daniel
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Active contours for tracking distributions
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|c 2004
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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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 12.10.2004
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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 A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments
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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, U.S. Gov't, Non-P.H.S.
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| 650 |
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|a Validation Study
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| 700 |
1 |
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|a Zhang, Tao
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 13(2004), 4 vom: 10. Apr., Seite 518-26
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
1 |
8 |
|g volume:13
|g year:2004
|g number:4
|g day:10
|g month:04
|g pages:518-26
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|a GBV_USEFLAG_A
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|a SYSFLAG_A
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|a GBV_NLM
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|a GBV_ILN_350
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| 951 |
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|a AR
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| 952 |
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|d 13
|j 2004
|e 4
|b 10
|c 04
|h 518-26
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