Ultimate levelings with strategy for filtering undesirable residues based on machine learning (2019)
- Authors:
- USP affiliated authors: HASHIMOTO, RONALDO FUMIO - IME ; SILVA, DÊNNIS JOSÉ DA - IME ; MORIMITSU, ALEXANDRE - IME
- Unidade: IME
- DOI: 10.1007/978-3-030-20867-7_23
- Subjects: PROCESSAMENTO DE IMAGENS; REGRESSÃO LINEAR
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Conference titles: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing -ISMM
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ALVES, Wonder Alexandre Luz et al. Ultimate levelings with strategy for filtering undesirable residues based on machine learning. 2019, Anais.. Cham: Springer, 2019. Disponível em: https://doi.org/10.1007/978-3-030-20867-7_23. Acesso em: 05 out. 2024. -
APA
Alves, W. A. L., Gobber, C. F., Silva, D. J. da, Morimitsu, A., Hashimoto, R. F., & Marcotegui, B. (2019). Ultimate levelings with strategy for filtering undesirable residues based on machine learning. In Mathematical morphology and its applications to signal and image processing: Proceedings. Cham: Springer. doi:10.1007/978-3-030-20867-7_23 -
NLM
Alves WAL, Gobber CF, Silva DJ da, Morimitsu A, Hashimoto RF, Marcotegui B. Ultimate levelings with strategy for filtering undesirable residues based on machine learning [Internet]. Mathematical morphology and its applications to signal and image processing: Proceedings. 2019 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/978-3-030-20867-7_23 -
Vancouver
Alves WAL, Gobber CF, Silva DJ da, Morimitsu A, Hashimoto RF, Marcotegui B. Ultimate levelings with strategy for filtering undesirable residues based on machine learning [Internet]. Mathematical morphology and its applications to signal and image processing: Proceedings. 2019 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1007/978-3-030-20867-7_23 - Incremental bit-quads count in tree of shapes
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Informações sobre o DOI: 10.1007/978-3-030-20867-7_23 (Fonte: oaDOI API)
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