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Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps

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  • معلومة اضافية
    • Contributors:
      Laboratoire d'Informatique Gaspard-Monge (LIGM); Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout (BEZOUT); Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS); PUC Minas
    • بيانات النشر:
      HAL CCSD
      Springer Verlag
    • الموضوع:
      2018
    • Collection:
      École des Ponts ParisTech: HAL
    • نبذة مختصرة :
      International audience ; Hierarchies of partitions are generally represented by dendrograms (direct representation). They can also be represented by saliency maps or minimum spanning trees. In this article, we precisely study the links between these three representations. In particular, we provide a new bijection between saliency maps and hierarchies based on quasi-flat zones as often used in image processing and we characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasi-flat zones preservation. In practice, these results make up a toolkit for designing new hierarchical methods where one can choose the most convenient representation. They also invite us to process non-image data with morphological hierarchies. More precisely, we show the practical interest of the proposed framework for: i) hierarchical watershed image segmentations, ii) combinations of dierent hierarchical segmentations, iii) hierarchicalizations of some non-hierarchical image segmentation methods based on regional dissimilarities, and iv) hierarchical analysis of geographical data.
    • Relation:
      hal-01344727; https://hal.science/hal-01344727; https://hal.science/hal-01344727v2/document; https://hal.science/hal-01344727v2/file/JC2.pdf
    • الرقم المعرف:
      10.1007/s10851-017-0768-7
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.C76E69B4