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231223s2004 xx ||||| 00| ||eng c |
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|a pubmed25n0500.xml
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|a (DE-627)NLM150057415
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|a (NLM)15326852
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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 Petrovic, Ana
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Multiresolution segmentation of natural images
|b from linear to nonlinear scale-space representations
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| 264 |
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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 14.09.2004
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|a Date Revised 26.10.2019
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| 500 |
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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 introduce a framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations. A non-linear scale-space stack is constructed by means of an appropriate diffusion equation. This stack is analyzed and a tree of coherent segments is constructed based on relationships between different scale layers. Pruning this tree proves to be a very efficient tool for unsupervised segmentation of different classes of images (e.g., natural, medical, etc.). This technique is light on the computational point of view and can be extended to nonscalar data in a straightforward manner
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| 650 |
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|a Comparative 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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| 700 |
1 |
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|a Escoda, Oscar Divorra
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Vandergheynst, Pierre
|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), 8 vom: 01. Aug., Seite 1104-14
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
1 |
8 |
|g volume:13
|g year:2004
|g number:8
|g day:01
|g month:08
|g pages:1104-14
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|a GBV_USEFLAG_A
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| 912 |
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|a SYSFLAG_A
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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 8
|b 01
|c 08
|h 1104-14
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