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Ultimate levelings with strategy for filtering undesirable residues based on machine learning

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Wonder Alves
  • Fonction : Auteur
  • PersonId : 1020851
Charles F Gobber
  • Fonction : Auteur
Dennis da Silva
  • Fonction : Auteur
Ronaldo F Hashimoto
  • Fonction : Auteur

Résumé

Ultimate levelings are operators that extract important image contrast information from a scale-space based on levelings. During the residual extraction process, it is very common that some residues are selected from undesirable regions, but they should be filtered out. In order to avoid this problem some strategies can be used to filter residues extracted by ultimate levelings. In this paper, we introduce a novel strategy to filter undesirable residues from ultimate levelings based on a regression model that predicts the correspondence between objects of interest and residual regions. In order to evaluate our new approach, some experiments were carried out with a plant dataset and the results show the robustness of our method.
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Dates et versions

hal-02430523 , version 1 (07-01-2020)

Identifiants

Citer

Wonder Alves, Charles F Gobber, Dennis da Silva, Alexandre Morimitsu, Ronaldo F Hashimoto, et al.. Ultimate levelings with strategy for filtering undesirable residues based on machine learning. 14th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, Jul 2019, Saarbrücken, Germany. ⟨10.1007/978-3-030-20867-7_23⟩. ⟨hal-02430523⟩
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