Combinatorial space of watershed hierarchies for image characterization
• We expose a framework for image characterization using hierarchies of segmentations. • We structure the space of hierarchies using the Gromov–Hausdorff distance. • We propose different ways of combining hierarchies and study their properties. • We leverage this combinatorial space to derive effici...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fehri, Amin [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: Thermal structure optimization of a supercondcuting cavity vertical test cryostat - Jin, Shufeng ELSEVIER, 2019, an official publ. of the International Association for Pattern Recognition, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:129 ; year:2020 ; pages:41-47 ; extent:7 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.patrec.2019.11.002 |
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ELV048938076 |
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| 520 | |a • We expose a framework for image characterization using hierarchies of segmentations. • We structure the space of hierarchies using the Gromov–Hausdorff distance. • We propose different ways of combining hierarchies and study their properties. • We leverage this combinatorial space to derive efficient image representations. | ||
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Combinatorial space of watershed hierarchies for image characterization |
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• We expose a framework for image characterization using hierarchies of segmentations. • We structure the space of hierarchies using the Gromov–Hausdorff distance. • We propose different ways of combining hierarchies and study their properties. • We leverage this combinatorial space to derive efficient image representations. |
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• We expose a framework for image characterization using hierarchies of segmentations. • We structure the space of hierarchies using the Gromov–Hausdorff distance. • We propose different ways of combining hierarchies and study their properties. • We leverage this combinatorial space to derive efficient image representations. |
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• We expose a framework for image characterization using hierarchies of segmentations. • We structure the space of hierarchies using the Gromov–Hausdorff distance. • We propose different ways of combining hierarchies and study their properties. • We leverage this combinatorial space to derive efficient image representations. |
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code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.patrec.2019.11.002</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.43</subfield><subfield code="j">Kältetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">33.09</subfield><subfield code="j">Physik unter besonderen Bedingungen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">129</subfield><subfield code="j">2020</subfield><subfield code="h">41-47</subfield><subfield code="g">7</subfield></datafield></record></collection>
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