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Ultimate Levelings with Strategy for Filtering Undesirable Residues Based on Machine Learning

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Luz Alves, Wonder Alexandre ; Gobber, Charles Ferreira ; da Silva, Dennis Jose ; Morimitsu, Alexandre ; Hashimoto, Ronaldo Fumio ; Marcotegui, Beatriz ; Burgeth, B ; Kleefeld, A ; Naegel, B ; Passat, N ; Perret, B
Número total de Autores: 11
Tipo de documento: Artigo Científico
Fonte: MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO SIGNAL AND IMAGE PROCESSING, ISMM 2019; v. 11564, p. 13-pg., 2019-01-01.
Resumo

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. (AU)

Processo FAPESP: 18/15652-7 - Segmentação de imagens baseada em restrições de formas por meio dos últimos levelings
Beneficiário:Wonder Alexandre Luz Alves
Modalidade de apoio: Auxílio à Pesquisa - Regular