000 | 03253nam a22005535i 4500 | ||
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001 | 978-3-319-53508-1 | ||
003 | DE-He213 | ||
005 | 20220801222013.0 | ||
007 | cr nn 008mamaa | ||
008 | 170314s2017 sz | s |||| 0|eng d | ||
020 |
_a9783319535081 _9978-3-319-53508-1 |
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024 | 7 |
_a10.1007/978-3-319-53508-1 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aTEC009000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aLast, Carsten. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _959170 |
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245 | 1 | 0 |
_aFrom Global to Local Statistical Shape Priors _h[electronic resource] : _bNovel Methods to Obtain Accurate Reconstruction Results with a Limited Amount of Training Shapes / _cby Carsten Last. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
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300 |
_aXXI, 259 p. 84 illus., 64 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v98 |
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505 | 0 | _aBasics -- Statistical Shape Models (SSMs) -- A Locally Deformable Statistical Shape Model (LDSSM) -- Evaluation of the Locally Deformable Statistical Shape Model -- Global-To-Local Shape Priors for Variational Level Set Methods -- Evaluation of the Global-To-Local Variational Formulation -- Conclusion and Outlook. | |
520 | _aThis book proposes a new approach to handle the problem of limited training data. Common approaches to cope with this problem are to model the shape variability independently across predefined segments or to allow artificial shape variations that cannot be explained through the training data, both of which have their drawbacks. The approach presented uses a local shape prior in each element of the underlying data domain and couples all local shape priors via smoothness constraints. The book provides a sound mathematical foundation in order to embed this new shape prior formulation into the well-known variational image segmentation framework. The new segmentation approach so obtained allows accurate reconstruction of even complex object classes with only a few training shapes at hand. | ||
650 | 0 |
_aComputational intelligence. _97716 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aImage processing—Digital techniques. _931565 |
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650 | 0 |
_aComputer vision. _959171 |
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650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569 |
710 | 2 |
_aSpringerLink (Online service) _959172 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319535074 |
776 | 0 | 8 |
_iPrinted edition: _z9783319535098 |
776 | 0 | 8 |
_iPrinted edition: _z9783319851693 |
830 | 0 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v98 _959173 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-53508-1 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
_c80294 _d80294 |