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Filtros : "Multimedia Tools and Applications" "Instituto Federal de São Paulo" Limpar

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  • Artigo de periodico

    Temporal video scene segmentation using deep-learning (2021)

    Trojahn, Tiago Henrique ; Goularte, Rudinei

    Source: Multimedia Tools and Applications. Unidade: ICMC

    Subjects: RECONHECIMENTO DE IMAGEM, VÍDEO, MULTIMÍDIA INTERATIVA, REDES NEURAIS, APRENDIZADO COMPUTACIONAL

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    • ABNT

      TROJAHN, Tiago Henrique e GOULARTE, Rudinei. Temporal video scene segmentation using deep-learning. Multimedia Tools and Applications, v. 80, n. 12, p. 17487-17513, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11042-020-10450-2. Acesso em: 12 nov. 2025.

    • APA

      Trojahn, T. H., & Goularte, R. (2021). Temporal video scene segmentation using deep-learning. Multimedia Tools and Applications, 80( 12), 17487-17513. doi:10.1007/s11042-020-10450-2

    • NLM

      Trojahn TH, Goularte R. Temporal video scene segmentation using deep-learning [Internet]. Multimedia Tools and Applications. 2021 ; 80( 12): 17487-17513.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s11042-020-10450-2

    • Vancouver

      Trojahn TH, Goularte R. Temporal video scene segmentation using deep-learning [Internet]. Multimedia Tools and Applications. 2021 ; 80( 12): 17487-17513.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s11042-020-10450-2

  • 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

    PrivadoAcesso à fonteDOIHow to cite
    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: 12 nov. 2025.

    • 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 2025 nov. 12 ] 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 2025 nov. 12 ] Available from: https://doi.org/10.1007/s11042-020-10384-9
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