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Artigo de periodico
Concept drift adaptation in video surveillance: a systematic review (2024)
Goncalves, Vinicius P. M.; Silva, Lourival P.; Marques, Fátima de Lourdes dos Santos Nunes ; Ferreira, João Eduardo ; Araújo, Luciano Vieira de
Source: Multimedia Tools and Applications. Unidades: STI, IME, EACH
Subjects: SEGURANÇA PÚBLICA, GRAVAÇÃO DE VÍDEO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GONCALVES, Vinicius P. M. et al. Concept drift adaptation in video surveillance: a systematic review. Multimedia Tools and Applications, v. 83, n. 4, p. 9997-10037, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11042-023-15855-3. Acesso em: 20 nov. 2025.
APA
Goncalves, V. P. M., Silva, L. P., Marques, F. de L. dos S. N., Ferreira, J. E., & Araújo, L. V. de. (2024). Concept drift adaptation in video surveillance: a systematic review. Multimedia Tools and Applications, 83( 4), 9997-10037. doi:10.1007/s11042-023-15855-3
NLM
Goncalves VPM, Silva LP, Marques F de L dos SN, Ferreira JE, Araújo LV de. Concept drift adaptation in video surveillance: a systematic review [Internet]. Multimedia Tools and Applications. 2024 ; 83( 4): 9997-10037.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-023-15855-3
Vancouver
Goncalves VPM, Silva LP, Marques F de L dos SN, Ferreira JE, Araújo LV de. Concept drift adaptation in video surveillance: a systematic review [Internet]. Multimedia Tools and Applications. 2024 ; 83( 4): 9997-10037.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-023-15855-3
Artigo de periodico
Scene designer: compositional sketch-based image retrieval with contrastive learning and an auxiliary synthesis task (2023)
Ribeiro, Leo Sampaio Ferraz ; Bui, Tu ; Collomosse, John ; Ponti, Moacir Antonelli
Source: Multimedia Tools and Applications. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE IMAGEM, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIBEIRO, Leo Sampaio Ferraz et al. Scene designer: compositional sketch-based image retrieval with contrastive learning and an auxiliary synthesis task. Multimedia Tools and Applications, v. 82, n. 24, p. 38117-38139, 2023Tradução . . Disponível em: https://doi.org/10.1007/s11042-022-14282-0. Acesso em: 20 nov. 2025.
APA
Ribeiro, L. S. F., Bui, T., Collomosse, J., & Ponti, M. A. (2023). Scene designer: compositional sketch-based image retrieval with contrastive learning and an auxiliary synthesis task. Multimedia Tools and Applications, 82( 24), 38117-38139. doi:10.1007/s11042-022-14282-0
NLM
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Scene designer: compositional sketch-based image retrieval with contrastive learning and an auxiliary synthesis task [Internet]. Multimedia Tools and Applications. 2023 ; 82( 24): 38117-38139.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-022-14282-0
Vancouver
Ribeiro LSF, Bui T, Collomosse J, Ponti MA. Scene designer: compositional sketch-based image retrieval with contrastive learning and an auxiliary synthesis task [Internet]. Multimedia Tools and Applications. 2023 ; 82( 24): 38117-38139.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-022-14282-0
Artigo de periodico
A river flooding detection system based on deep learning and computer vision (2022)
Fernandes Junior, Francisco Erivaldo ; Nonato, Luis Gustavo ; Ueyama, Jó
Source: Multimedia Tools and Applications. Unidade: ICMC
Subjects: VISÃO COMPUTACIONAL, ENCHENTES URBANAS, SEMÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDES JUNIOR, Francisco Erivaldo e NONATO, Luis Gustavo e UEYAMA, Jó. A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, v. 81, p. 40231-40251, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11042-022-12813-3. Acesso em: 20 nov. 2025.
APA
Fernandes Junior, F. E., Nonato, L. G., & Ueyama, J. (2022). A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, 81, 40231-40251. doi:10.1007/s11042-022-12813-3
NLM
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-022-12813-3
Vancouver
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1007/s11042-022-12813-3
Artigo de periodico
Object classification using a local texture descriptor and a support vector machine (2017)
Ferraz, Carolina Toledo ; Gonzaga, Adilson
Source: Multimedia Tools and Applications. Unidade: EESC
Subjects: PROCESSAMENTO DE IMAGENS, RECONHECIMENTO DE OBJETOS, RECONHECIMENTO DE PADRÕES, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERRAZ, Carolina Toledo e GONZAGA, Adilson. Object classification using a local texture descriptor and a support vector machine. Multimedia Tools and Applications, v. 76, p. 20609–20641, 2017Tradução . . Disponível em: http://dx.doi.org/10.1007/s11042-016-4003-0. Acesso em: 20 nov. 2025.
APA
Ferraz, C. T., & Gonzaga, A. (2017). Object classification using a local texture descriptor and a support vector machine. Multimedia Tools and Applications, 76, 20609–20641. doi:10.1007/s11042-016-4003-0
NLM
Ferraz CT, Gonzaga A. Object classification using a local texture descriptor and a support vector machine [Internet]. Multimedia Tools and Applications. 2017 ; 76 20609–20641.[citado 2025 nov. 20 ] Available from: http://dx.doi.org/10.1007/s11042-016-4003-0
Vancouver
Ferraz CT, Gonzaga A. Object classification using a local texture descriptor and a support vector machine [Internet]. Multimedia Tools and Applications. 2017 ; 76 20609–20641.[citado 2025 nov. 20 ] Available from: http://dx.doi.org/10.1007/s11042-016-4003-0

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