Ultimate leveling based on Mumford-Shah energy functional applied to plant detection (2018)
- Authors:
- Autor USP: HASHIMOTO, RONALDO FUMIO - IME
- Unidade: IME
- DOI: 10.1007/978-3-319-75193-1_27
- Subjects: COMPUTAÇÃO GRÁFICA; PROCESSAMENTO DE IMAGENS
- Keywords: ultimate levelings; morphological trees; Mumford-Shah energy functional; plant detection
- Language: Inglês
- Imprenta:
- Source:
- Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GOBBER, Charles e ALVES, Wonder Alexandre Luz e HASHIMOTO, Ronaldo Fumio. Ultimate leveling based on Mumford-Shah energy functional applied to plant detection. 2018, Anais.. Cham: Springer, 2018. Disponível em: https://doi.org/10.1007/978-3-319-75193-1_27. Acesso em: 21 jan. 2026. -
APA
Gobber, C., Alves, W. A. L., & Hashimoto, R. F. (2018). Ultimate leveling based on Mumford-Shah energy functional applied to plant detection. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 22nd Iberoamerican Congress, CIARP 2017 Valparaíso, Chile, November 7–10, 2017 Proceedings. Cham: Springer. doi:10.1007/978-3-319-75193-1_27 -
NLM
Gobber C, Alves WAL, Hashimoto RF. Ultimate leveling based on Mumford-Shah energy functional applied to plant detection [Internet]. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 22nd Iberoamerican Congress, CIARP 2017 Valparaíso, Chile, November 7–10, 2017 Proceedings. 2018 ;[citado 2026 jan. 21 ] Available from: https://doi.org/10.1007/978-3-319-75193-1_27 -
Vancouver
Gobber C, Alves WAL, Hashimoto RF. Ultimate leveling based on Mumford-Shah energy functional applied to plant detection [Internet]. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 22nd Iberoamerican Congress, CIARP 2017 Valparaíso, Chile, November 7–10, 2017 Proceedings. 2018 ;[citado 2026 jan. 21 ] Available from: https://doi.org/10.1007/978-3-319-75193-1_27 - Inference of restricted stochastic Boolean GRN' s by Bayesian error and entropy based criteria
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Informações sobre o DOI: 10.1007/978-3-319-75193-1_27 (Fonte: oaDOI API)
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