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Artigo de periodico
A comparison among keyframe extraction techniques for CNN classification based on video periocular images (2021)
Ferraz, Carolina Toledo ; Barcellos, William; Pereira Junior, Osmando ; Borges, Tamiris Trevisan Negri ; Manzato, Marcelo Garcia ; Gonzaga, Adilson ; Saito, Jose Hiroki
Source: Multimedia Tools and Applications. Unidades: ICMC, EESC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, OLHO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERRAZ, Carolina Toledo et al. A comparison among keyframe extraction techniques for CNN classification based on video periocular images. Multimedia Tools and Applications, v. 80, n. 8, p. 12843-12856, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11042-020-10384-9. Acesso em: 06 set. 2024.
APA
Ferraz, C. T., Barcellos, W., Pereira Junior, O., Borges, T. T. N., Manzato, M. G., Gonzaga, A., & Saito, J. H. (2021). A comparison among keyframe extraction techniques for CNN classification based on video periocular images. Multimedia Tools and Applications, 80( 8), 12843-12856. doi:10.1007/s11042-020-10384-9
NLM
Ferraz CT, Barcellos W, Pereira Junior O, Borges TTN, Manzato MG, Gonzaga A, Saito JH. A comparison among keyframe extraction techniques for CNN classification based on video periocular images [Internet]. Multimedia Tools and Applications. 2021 ; 80( 8): 12843-12856.[citado 2024 set. 06 ] Available from: https://doi.org/10.1007/s11042-020-10384-9
Vancouver
Ferraz CT, Barcellos W, Pereira Junior O, Borges TTN, Manzato MG, Gonzaga A, Saito JH. A comparison among keyframe extraction techniques for CNN classification based on video periocular images [Internet]. Multimedia Tools and Applications. 2021 ; 80( 8): 12843-12856.[citado 2024 set. 06 ] Available from: https://doi.org/10.1007/s11042-020-10384-9
Trabalho de evento
Evaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions (2018)
Ferraz, Carolina Toledo ; Borges, Tamiris Trevisan Negri ; Cavichiolli, Adriane; Gonzaga, Adilson ; Saito, Jose Hiroki
Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images- SIBGRAPI. Unidade: EESC
Subjects: REDES NEURAIS, ILUMINAÇÃO, ALIMENTOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERRAZ, Carolina Toledo et al. Evaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions. 2018, Anais.. Porto Alegre: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/b8ee5039-0201-4853-a7af-c70ad283e3c9/sysno3190748_trabalho%2008%20-%20Evaluation%20of%20convolutional%20neural%20networks%20for%20raw%20food%20texture%20classification%20under%20variations%20of%20lighting%20conditions.%20%28SIBGRAPI%2C%202018%29.pdf. Acesso em: 06 set. 2024.
APA
Ferraz, C. T., Borges, T. T. N., Cavichiolli, A., Gonzaga, A., & Saito, J. H. (2018). Evaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions. In Proceedings. Porto Alegre: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/b8ee5039-0201-4853-a7af-c70ad283e3c9/sysno3190748_trabalho%2008%20-%20Evaluation%20of%20convolutional%20neural%20networks%20for%20raw%20food%20texture%20classification%20under%20variations%20of%20lighting%20conditions.%20%28SIBGRAPI%2C%202018%29.pdf
NLM
Ferraz CT, Borges TTN, Cavichiolli A, Gonzaga A, Saito JH. Evaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions [Internet]. Proceedings. 2018 ;[citado 2024 set. 06 ] Available from: https://repositorio.usp.br/directbitstream/b8ee5039-0201-4853-a7af-c70ad283e3c9/sysno3190748_trabalho%2008%20-%20Evaluation%20of%20convolutional%20neural%20networks%20for%20raw%20food%20texture%20classification%20under%20variations%20of%20lighting%20conditions.%20%28SIBGRAPI%2C%202018%29.pdf
Vancouver
Ferraz CT, Borges TTN, Cavichiolli A, Gonzaga A, Saito JH. Evaluation of convolutional neural networks for raw food texture classification under variations of lighting conditions [Internet]. Proceedings. 2018 ;[citado 2024 set. 06 ] Available from: https://repositorio.usp.br/directbitstream/b8ee5039-0201-4853-a7af-c70ad283e3c9/sysno3190748_trabalho%2008%20-%20Evaluation%20of%20convolutional%20neural%20networks%20for%20raw%20food%20texture%20classification%20under%20variations%20of%20lighting%20conditions.%20%28SIBGRAPI%2C%202018%29.pdf
Trabalho de evento
Robustness of rotation invariant descriptors for texture classification (2016)
Vieira, Raissa Tavares; Negri, Tamiris Trevisan; Gonzaga, Adilson
Source: Proceedings, Part I. Conference titles: International Symposium Advances in Visual Computing - ISVC 2016. Unidade: EESC
Subjects: VISÃO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VIEIRA, Raissa Tavares e NEGRI, Tamiris Trevisan e GONZAGA, Adilson. Robustness of rotation invariant descriptors for texture classification. 2016, Anais.. Cham: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2016. Disponível em: http://dx.doi.org/10.1007/978-3-319-50835-1_25. Acesso em: 06 set. 2024.
APA
Vieira, R. T., Negri, T. T., & Gonzaga, A. (2016). Robustness of rotation invariant descriptors for texture classification. In Proceedings, Part I. Cham: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1007/978-3-319-50835-1_25
NLM
Vieira RT, Negri TT, Gonzaga A. Robustness of rotation invariant descriptors for texture classification [Internet]. Proceedings, Part I. 2016 ;[citado 2024 set. 06 ] Available from: http://dx.doi.org/10.1007/978-3-319-50835-1_25
Vancouver
Vieira RT, Negri TT, Gonzaga A. Robustness of rotation invariant descriptors for texture classification [Internet]. Proceedings, Part I. 2016 ;[citado 2024 set. 06 ] Available from: http://dx.doi.org/10.1007/978-3-319-50835-1_25

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