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  • Source: Multimedia Tools and Applications. Unidades: ICMC, EESC

    Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, OLHO

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      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: 18 jun. 2024.
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      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 jun. 18 ] 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 jun. 18 ] Available from: https://doi.org/10.1007/s11042-020-10384-9
  • Source: Pattern Analysis and Applications. Unidade: EESC

    Subjects: ÍRIS, RECONHECIMENTO DE IMAGEM, VISÃO

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      LANGONI, Virgílio de Melo e GONZAGA, Adilson. Evaluating dynamic texture descriptors to recognize human iris in video image sequence. Pattern Analysis and Applications, v. 23, p. 771-784, 2020Tradução . . Disponível em: http://dx.org.br/10.1007/s10044-019-00836-w. Acesso em: 18 jun. 2024.
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      Langoni, V. de M., & Gonzaga, A. (2020). Evaluating dynamic texture descriptors to recognize human iris in video image sequence. Pattern Analysis and Applications, 23, 771-784. doi:10.1007/s10044-019-00836-w
    • NLM

      Langoni V de M, Gonzaga A. Evaluating dynamic texture descriptors to recognize human iris in video image sequence [Internet]. Pattern Analysis and Applications. 2020 ; 23 771-784.[citado 2024 jun. 18 ] Available from: http://dx.org.br/10.1007/s10044-019-00836-w
    • Vancouver

      Langoni V de M, Gonzaga A. Evaluating dynamic texture descriptors to recognize human iris in video image sequence [Internet]. Pattern Analysis and Applications. 2020 ; 23 771-784.[citado 2024 jun. 18 ] Available from: http://dx.org.br/10.1007/s10044-019-00836-w
  • Source: Journal of Digital Imaging. Unidade: EESC

    Subjects: ENGENHARIA ELÉTRICA, MAMOGRAFIA, IMAGEM DIGITAL

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      PEREIRA JUNIOR, Osmando et al. A novel fusion-based texture descriptor to improve the detection of architectural distortion in digital mammography. Journal of Digital Imaging, p. 1-17, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10278-020-00391-5. Acesso em: 18 jun. 2024.
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      Pereira Junior, O., Oliveira, H. C. R. de, Ferraz, C. T., Saito, J. H., Vieira, M. A. da C., & Gonzaga, A. (2020). A novel fusion-based texture descriptor to improve the detection of architectural distortion in digital mammography. Journal of Digital Imaging, 1-17. doi:10.1007/s10278-020-00391-5
    • NLM

      Pereira Junior O, Oliveira HCR de, Ferraz CT, Saito JH, Vieira MA da C, Gonzaga A. A novel fusion-based texture descriptor to improve the detection of architectural distortion in digital mammography [Internet]. Journal of Digital Imaging. 2020 ; 1-17.[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s10278-020-00391-5
    • Vancouver

      Pereira Junior O, Oliveira HCR de, Ferraz CT, Saito JH, Vieira MA da C, Gonzaga A. A novel fusion-based texture descriptor to improve the detection of architectural distortion in digital mammography [Internet]. Journal of Digital Imaging. 2020 ; 1-17.[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s10278-020-00391-5
  • Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: EESC

    Subjects: BIOMETRIA, REDES NEURAIS, TRANSFERÊNCIA (APRENDIZAGEM), ENGENHARIA ELÉTRICA

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      DALAPICOLA, Rodolfo Coelho et al. Impact of facial expressions on the accuracy of a CNN performing periocular recognition. 2019, Anais.. Piscataway, NJ, USA: IEEE, 2019. Disponível em: https://doi.org/10.1109/BRACIS.2019.00077. Acesso em: 18 jun. 2024.
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      Dalapicola, R. C., Queiroga, R. T. V., Ferraz, C. T., Borges, T. T. N., Saito, J. H., & Gonzaga, A. (2019). Impact of facial expressions on the accuracy of a CNN performing periocular recognition. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/BRACIS.2019.00077
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      Dalapicola RC, Queiroga RTV, Ferraz CT, Borges TTN, Saito JH, Gonzaga A. Impact of facial expressions on the accuracy of a CNN performing periocular recognition [Internet]. Proceedings. 2019 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1109/BRACIS.2019.00077
    • Vancouver

      Dalapicola RC, Queiroga RTV, Ferraz CT, Borges TTN, Saito JH, Gonzaga A. Impact of facial expressions on the accuracy of a CNN performing periocular recognition [Internet]. Proceedings. 2019 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1109/BRACIS.2019.00077
  • Source: Proceedings. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: PERCEPÇÃO DA FACE, VISÃO COMPUTACIONAL, ENGENHARIA ELÉTRICA

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      BARCELLOS, William et al. Evaluation of fine tuning and feature extraction methods in biometric periocular recognition. 2019, Anais.. Porto Alegre, RS: SBC, 2019. . Acesso em: 18 jun. 2024.
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      Barcellos, W., Shitara, N. H., Ferraz, C. T., Queiroga, R. T. V., Saito, J. H., & Gonzaga, A. (2019). Evaluation of fine tuning and feature extraction methods in biometric periocular recognition. In Proceedings. Porto Alegre, RS: SBC.
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      Barcellos W, Shitara NH, Ferraz CT, Queiroga RTV, Saito JH, Gonzaga A. Evaluation of fine tuning and feature extraction methods in biometric periocular recognition. Proceedings. 2019 ;[citado 2024 jun. 18 ]
    • Vancouver

      Barcellos W, Shitara NH, Ferraz CT, Queiroga RTV, Saito JH, Gonzaga A. Evaluation of fine tuning and feature extraction methods in biometric periocular recognition. Proceedings. 2019 ;[citado 2024 jun. 18 ]
  • Source: Biomedical signal processing and control. Unidades: FM, EESC

    Subjects: MAMOGRAFIA, NEOPLASIAS MAMÁRIAS, INTENSIFICAÇÃO DE IMAGEM RADIOGRÁFICA

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      OLIVEIRA, Helder Cesar Rodrigues de et al. A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices. Biomedical signal processing and control, v. 50, p. 92-102, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2019.01.001. Acesso em: 18 jun. 2024.
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      Oliveira, H. C. R. de, Mencattini, A., Casti, P., Catani, J. H., Barros, N. de, Gonzaga, A., et al. (2019). A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices. Biomedical signal processing and control, 50, 92-102. doi:10.1016/j.bspc.2019.01.001
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      Oliveira HCR de, Mencattini A, Casti P, Catani JH, Barros N de, Gonzaga A, Martinelli E, Vieira MA da C. A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices [Internet]. Biomedical signal processing and control. 2019 ; 50 92-102.[citado 2024 jun. 18 ] Available from: https://doi.org/10.1016/j.bspc.2019.01.001
    • Vancouver

      Oliveira HCR de, Mencattini A, Casti P, Catani JH, Barros N de, Gonzaga A, Martinelli E, Vieira MA da C. A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices [Internet]. Biomedical signal processing and control. 2019 ; 50 92-102.[citado 2024 jun. 18 ] Available from: https://doi.org/10.1016/j.bspc.2019.01.001
  • Source: Multimedia Tools and Applications. Unidade: EESC

    Subjects: ÍRIS, BIOMETRIA, PUPILA, ENGENHARIA ELÉTRICA

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      SOUZA, Jones Mendonça de e GONZAGA, Adilson. Human iris feature extraction under pupil size variation using local texture descriptors. Multimedia Tools and Applications, 2019Tradução . . Disponível em: https://doi.org/10.1007/s11042-019-7371-4. Acesso em: 18 jun. 2024.
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      Souza, J. M. de, & Gonzaga, A. (2019). Human iris feature extraction under pupil size variation using local texture descriptors. Multimedia Tools and Applications. doi:10.1007/s11042-019-7371-4
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      Souza JM de, Gonzaga A. Human iris feature extraction under pupil size variation using local texture descriptors [Internet]. Multimedia Tools and Applications. 2019 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s11042-019-7371-4
    • Vancouver

      Souza JM de, Gonzaga A. Human iris feature extraction under pupil size variation using local texture descriptors [Internet]. Multimedia Tools and Applications. 2019 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s11042-019-7371-4
  • Source: Proceedings of SPIE. Conference titles: Medical Imaging 2018 : image perception, observer performance, and technology assessment. Unidade: EESC

    Subjects: IMAGEM DIGITAL, MAMOGRAFIA, CAD, ENGENHARIA ELÉTRICA

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      OLIVEIRA, Helder C. R. de et al. Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography. Proceedings of SPIE. Bellingham, United States: SPIE. Disponível em: https://doi.org/10.1117/12.2293388. Acesso em: 18 jun. 2024. , 2018
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      Oliveira, H. C. R. de, Mencattini, A., Casti, P., Martinelli, E., Natale, C. di, Catani, J. H., et al. (2018). Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography. Proceedings of SPIE. Bellingham, United States: SPIE. doi:10.1117/12.2293388
    • NLM

      Oliveira HCR de, Mencattini A, Casti P, Martinelli E, Natale C di, Catani JH, Barros N de, Melo CFE, Gonzaga A, Vieira MA da C. Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography [Internet]. Proceedings of SPIE. 2018 ; 10575[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/12.2293388
    • Vancouver

      Oliveira HCR de, Mencattini A, Casti P, Martinelli E, Natale C di, Catani JH, Barros N de, Melo CFE, Gonzaga A, Vieira MA da C. Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography [Internet]. Proceedings of SPIE. 2018 ; 10575[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/12.2293388
  • Source: Anais. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: BIOMETRIA, ÍRIS

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      SOUZA, Jones Mendonça de e GONZAGA, Adilson. Biometric iris classification when the eye’s pupil reacts to light. 2018, Anais.. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/bcafa583-8861-49a2-a263-539ad1967f42/sysno3190684_trabalho%2014%20-%20Biometric%20iris%20classification%20when%20the%20eye%E2%80%99s%20pupil%20reacts%20to%20light.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29.pdf. Acesso em: 18 jun. 2024.
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      Souza, J. M. de, & Gonzaga, A. (2018). Biometric iris classification when the eye’s pupil reacts to light. In Anais. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/bcafa583-8861-49a2-a263-539ad1967f42/sysno3190684_trabalho%2014%20-%20Biometric%20iris%20classification%20when%20the%20eye%E2%80%99s%20pupil%20reacts%20to%20light.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29.pdf
    • NLM

      Souza JM de, Gonzaga A. Biometric iris classification when the eye’s pupil reacts to light [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/bcafa583-8861-49a2-a263-539ad1967f42/sysno3190684_trabalho%2014%20-%20Biometric%20iris%20classification%20when%20the%20eye%E2%80%99s%20pupil%20reacts%20to%20light.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29.pdf
    • Vancouver

      Souza JM de, Gonzaga A. Biometric iris classification when the eye’s pupil reacts to light [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/bcafa583-8861-49a2-a263-539ad1967f42/sysno3190684_trabalho%2014%20-%20Biometric%20iris%20classification%20when%20the%20eye%E2%80%99s%20pupil%20reacts%20to%20light.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29.pdf
  • Source: Proceedings. Conference titles: Conference on Graphics, Patterns and Images- SIBGRAPI. Unidade: EESC

    Subjects: REDES NEURAIS, ILUMINAÇÃO, ALIMENTOS

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      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: 18 jun. 2024.
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      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
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      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 jun. 18 ] 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 jun. 18 ] 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
  • Source: Anais. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: PROCESSAMENTO DE IMAGENS, APRENDIZAGEM PROFUNDA, REDES NEURAIS, RECONHECIMENTO DE IMAGEM

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      FACCINI, Fábio Augusto Gonçalves e GONZAGA, Adilson. Accuracy performance evaluation of a CNN for classifying images corrupted by noise. 2018, Anais.. [S.l.]: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/7ea2a591-6cc2-40e3-960d-4dbcc8c16778/sysno3190572_trabalho%2007%20-%20Accuracy%20performance%20evaluation%20of%20a%20CNN%20for%20classifying%20images%20corrupted%20by%20noise.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf. Acesso em: 18 jun. 2024.
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      Faccini, F. A. G., & Gonzaga, A. (2018). Accuracy performance evaluation of a CNN for classifying images corrupted by noise. In Anais. Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/7ea2a591-6cc2-40e3-960d-4dbcc8c16778/sysno3190572_trabalho%2007%20-%20Accuracy%20performance%20evaluation%20of%20a%20CNN%20for%20classifying%20images%20corrupted%20by%20noise.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
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      Faccini FAG, Gonzaga A. Accuracy performance evaluation of a CNN for classifying images corrupted by noise [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/7ea2a591-6cc2-40e3-960d-4dbcc8c16778/sysno3190572_trabalho%2007%20-%20Accuracy%20performance%20evaluation%20of%20a%20CNN%20for%20classifying%20images%20corrupted%20by%20noise.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
    • Vancouver

      Faccini FAG, Gonzaga A. Accuracy performance evaluation of a CNN for classifying images corrupted by noise [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/7ea2a591-6cc2-40e3-960d-4dbcc8c16778/sysno3190572_trabalho%2007%20-%20Accuracy%20performance%20evaluation%20of%20a%20CNN%20for%20classifying%20images%20corrupted%20by%20noise.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
  • Source: Journal of Electronic Imaging. Unidade: EESC

    Subjects: ILUMINAÇÃO, IMAGEM, ENGENHARIA ELÉTRICA

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      NEGRI, Tamiris Trevisan et al. Extended color local mapped pattern for color texture classification under varying illumination. Journal of Electronic Imaging, v. 27, n. Ja/Feb. 2018, p. 011008(1-12), 2018Tradução . . Disponível em: https://doi.org/10.1117/1.JEI.27.1.011008. Acesso em: 18 jun. 2024.
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      Negri, T. T., Zhou, F., Obradovic, Z., & Gonzaga, A. (2018). Extended color local mapped pattern for color texture classification under varying illumination. Journal of Electronic Imaging, 27( Ja/Feb. 2018), 011008(1-12). doi:10.1117/1.JEI.27.1.011008
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      Negri TT, Zhou F, Obradovic Z, Gonzaga A. Extended color local mapped pattern for color texture classification under varying illumination [Internet]. Journal of Electronic Imaging. 2018 ; 27( Ja/Feb. 2018): 011008(1-12).[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/1.JEI.27.1.011008
    • Vancouver

      Negri TT, Zhou F, Obradovic Z, Gonzaga A. Extended color local mapped pattern for color texture classification under varying illumination [Internet]. Journal of Electronic Imaging. 2018 ; 27( Ja/Feb. 2018): 011008(1-12).[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/1.JEI.27.1.011008
  • Source: Anais. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: RETINA, VISÃO

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      LOURO, Antonio H. F. e GONZAGA, Adilson. Detection of curvatures in the retina. Would it be possible? 2018, Anais.. [S.l.]: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/0436d70e-ea7a-4e4c-8a72-4fc9a10cb815/sysno3190605_trabalho%2006%20-%20Detection%20of%20Curvatures%20in%20the%20Retina.%20Would%20it%20be%20Possible.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf. Acesso em: 18 jun. 2024.
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      Louro, A. H. F., & Gonzaga, A. (2018). Detection of curvatures in the retina. Would it be possible? In Anais. Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/0436d70e-ea7a-4e4c-8a72-4fc9a10cb815/sysno3190605_trabalho%2006%20-%20Detection%20of%20Curvatures%20in%20the%20Retina.%20Would%20it%20be%20Possible.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
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      Louro AHF, Gonzaga A. Detection of curvatures in the retina. Would it be possible? [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/0436d70e-ea7a-4e4c-8a72-4fc9a10cb815/sysno3190605_trabalho%2006%20-%20Detection%20of%20Curvatures%20in%20the%20Retina.%20Would%20it%20be%20Possible.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
    • Vancouver

      Louro AHF, Gonzaga A. Detection of curvatures in the retina. Would it be possible? [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/0436d70e-ea7a-4e4c-8a72-4fc9a10cb815/sysno3190605_trabalho%2006%20-%20Detection%20of%20Curvatures%20in%20the%20Retina.%20Would%20it%20be%20Possible.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202018%29.pdf
  • Source: Proceedings, v. 4. Conference titles: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP 2017. Unidade: EESC

    Subjects: VISÃO COMPUTACIONAL, ILUMINAÇÃO, IMAGEM

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      NEGRI, Tamiris Trevisan et al. A robust descriptor for color texture classification under varying illumination. 2018, Anais.. Setúbal: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: http://dx.doi.org/10.5220/0006143403780388. Acesso em: 18 jun. 2024.
    • APA

      Negri, T. T., Zhou, F., Obradovic, Z., & Gonzaga, A. (2018). A robust descriptor for color texture classification under varying illumination. In Proceedings, v. 4. Setúbal: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.5220/0006143403780388
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      Negri TT, Zhou F, Obradovic Z, Gonzaga A. A robust descriptor for color texture classification under varying illumination [Internet]. Proceedings, v. 4. 2018 ;[citado 2024 jun. 18 ] Available from: http://dx.doi.org/10.5220/0006143403780388
    • Vancouver

      Negri TT, Zhou F, Obradovic Z, Gonzaga A. A robust descriptor for color texture classification under varying illumination [Internet]. Proceedings, v. 4. 2018 ;[citado 2024 jun. 18 ] Available from: http://dx.doi.org/10.5220/0006143403780388
  • Source: Anais. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: PROCESSAMENTO DE IMAGENS, REDES NEURAIS

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      MORAES, Diego Rafael e GONZAGA, Adilson. Multi-class segmentation of satellite images by color mixture and neural network. 2018, Anais.. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/fc0a6432-b78d-44db-9a26-46d594f402c7/sysno3190685_trabalho%2013%20-%20Multi-class%20Segmentation%20of%20Satellite%20Images%20by%20Color%20Mixture%20and%20Neural%20Network.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf. Acesso em: 18 jun. 2024.
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      Moraes, D. R., & Gonzaga, A. (2018). Multi-class segmentation of satellite images by color mixture and neural network. In Anais. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/fc0a6432-b78d-44db-9a26-46d594f402c7/sysno3190685_trabalho%2013%20-%20Multi-class%20Segmentation%20of%20Satellite%20Images%20by%20Color%20Mixture%20and%20Neural%20Network.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
    • NLM

      Moraes DR, Gonzaga A. Multi-class segmentation of satellite images by color mixture and neural network [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/fc0a6432-b78d-44db-9a26-46d594f402c7/sysno3190685_trabalho%2013%20-%20Multi-class%20Segmentation%20of%20Satellite%20Images%20by%20Color%20Mixture%20and%20Neural%20Network.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
    • Vancouver

      Moraes DR, Gonzaga A. Multi-class segmentation of satellite images by color mixture and neural network [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/fc0a6432-b78d-44db-9a26-46d594f402c7/sysno3190685_trabalho%2013%20-%20Multi-class%20Segmentation%20of%20Satellite%20Images%20by%20Color%20Mixture%20and%20Neural%20Network.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
  • Source: Anais. Conference titles: Workshop de Visão Computacional - WVC. Unidade: EESC

    Subjects: VISÃO COMPUTACIONAL, IMAGEM, ILUMINAÇÃO

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      NEGRI, Tamiris Trevisan e VIEIRA, Raissa Tavares e GONZAGA, Adilson. Color texture classification by using opponent color and local mapped pattern. 2018, Anais.. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: https://repositorio.usp.br/directbitstream/eb0f6b63-35ec-4d9b-a57e-1bbe4d4827eb/sysno3190678_trabalho%2015%20-%20Color%20Texture%20Classification%20by%20using%20Opponent%20Color%20and%20Local%20Mapped%20Pattern.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf. Acesso em: 18 jun. 2024.
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      Negri, T. T., Vieira, R. T., & Gonzaga, A. (2018). Color texture classification by using opponent color and local mapped pattern. In Anais. Natal: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/eb0f6b63-35ec-4d9b-a57e-1bbe4d4827eb/sysno3190678_trabalho%2015%20-%20Color%20Texture%20Classification%20by%20using%20Opponent%20Color%20and%20Local%20Mapped%20Pattern.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
    • NLM

      Negri TT, Vieira RT, Gonzaga A. Color texture classification by using opponent color and local mapped pattern [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/eb0f6b63-35ec-4d9b-a57e-1bbe4d4827eb/sysno3190678_trabalho%2015%20-%20Color%20Texture%20Classification%20by%20using%20Opponent%20Color%20and%20Local%20Mapped%20Pattern.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
    • Vancouver

      Negri TT, Vieira RT, Gonzaga A. Color texture classification by using opponent color and local mapped pattern [Internet]. Anais. 2018 ;[citado 2024 jun. 18 ] Available from: https://repositorio.usp.br/directbitstream/eb0f6b63-35ec-4d9b-a57e-1bbe4d4827eb/sysno3190678_trabalho%2015%20-%20Color%20Texture%20Classification%20by%20using%20Opponent%20Color%20and%20Local%20Mapped%20Pattern.%20%28Workshop%20de%20Vis%C3%A3o%20Computacional%2C%202017%29%20%281%29.pdf
  • Source: Multimedia Tools and Applications. Unidade: EESC

    Subjects: RECONHECIMENTO DE IMAGEM, ENGENHARIA ELÉTRICA

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      VIEIRA, Raissa Tavares e NEGRI, Tamiris Trevisan e GONZAGA, Adilson. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor. Multimedia Tools and Applications, 2018Tradução . . Disponível em: https://doi.org/10.1007/s11042-018-6204-1. Acesso em: 18 jun. 2024.
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      Vieira, R. T., Negri, T. T., & Gonzaga, A. (2018). Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor. Multimedia Tools and Applications. doi:10.1007/s11042-018-6204-1
    • NLM

      Vieira RT, Negri TT, Gonzaga A. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor [Internet]. Multimedia Tools and Applications. 2018 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s11042-018-6204-1
    • Vancouver

      Vieira RT, Negri TT, Gonzaga A. Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor [Internet]. Multimedia Tools and Applications. 2018 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1007/s11042-018-6204-1
  • Source: Proceedings. Conference titles: Workshop of Computer Vision -WVC. Unidade: EESC

    Subjects: PROCESSAMENTO DE IMAGENS, CÉLULAS EPITELIAIS

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      VIEIRA, Raissa Tavares et al. Human epithelial type 2 cell classification using a multiresolution texture descriptor. 2018, Anais.. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2018. Disponível em: http://dx.doi.org/10.1109/WVC.2017.00008. Acesso em: 18 jun. 2024.
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      Vieira, R. T., Negri, T. T., Cavichiolli, A., & Gonzaga, A. (2018). Human epithelial type 2 cell classification using a multiresolution texture descriptor. In Proceedings. Danvers, MA: Escola de Engenharia de São Carlos, Universidade de São Paulo. doi:10.1109/WVC.2017.00008
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      Vieira RT, Negri TT, Cavichiolli A, Gonzaga A. Human epithelial type 2 cell classification using a multiresolution texture descriptor [Internet]. Proceedings. 2018 ;[citado 2024 jun. 18 ] Available from: http://dx.doi.org/10.1109/WVC.2017.00008
    • Vancouver

      Vieira RT, Negri TT, Cavichiolli A, Gonzaga A. Human epithelial type 2 cell classification using a multiresolution texture descriptor [Internet]. Proceedings. 2018 ;[citado 2024 jun. 18 ] Available from: http://dx.doi.org/10.1109/WVC.2017.00008
  • Source: Proceedings. Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia. Unidades: ICMC, EESC

    Subjects: RECONHECIMENTO DE IMAGEM, TEXTURA, RECUPERAÇÃO DA INFORMAÇÃO

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      FERRAZ, Carolina Toledo e MANZATO, Marcelo Garcia e GONZAGA, Adilson. Face classification using a new local texture descriptor. 2017, Anais.. New York: ACM, 2017. Disponível em: https://doi.org/10.1145/3126858.3131584. Acesso em: 18 jun. 2024.
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      Ferraz, C. T., Manzato, M. G., & Gonzaga, A. (2017). Face classification using a new local texture descriptor. In Proceedings. New York: ACM. doi:10.1145/3126858.3131584
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      Ferraz CT, Manzato MG, Gonzaga A. Face classification using a new local texture descriptor [Internet]. Proceedings. 2017 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1145/3126858.3131584
    • Vancouver

      Ferraz CT, Manzato MG, Gonzaga A. Face classification using a new local texture descriptor [Internet]. Proceedings. 2017 ;[citado 2024 jun. 18 ] Available from: https://doi.org/10.1145/3126858.3131584
  • Source: Proceedings of SPIE. Conference titles: Medical Imaging 2017 : image perception, observer performance, and technology assessment. Unidade: EESC

    Subjects: MAMOGRAFIA, ENGENHARIA ELÉTRICA

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      OLIVEIRA, Helder C. R. de et al. A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images. Proceedings of SPIE. Bellingham, United States: SPIE. Disponível em: https://doi.org/10.1117/12.2255516. Acesso em: 18 jun. 2024. , 2017
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      Oliveira, H. C. R. de, Moraes, D. R., Reche, G. A., Borges, L. R., Catani, J. H., Barros, N. de, et al. (2017). A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images. Proceedings of SPIE. Bellingham, United States: SPIE. doi:10.1117/12.2255516
    • NLM

      Oliveira HCR de, Moraes DR, Reche GA, Borges LR, Catani JH, Barros N de, Melo CFE, Gonzaga A, Vieira MA da C. A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images [Internet]. Proceedings of SPIE. 2017 ; 10134[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/12.2255516
    • Vancouver

      Oliveira HCR de, Moraes DR, Reche GA, Borges LR, Catani JH, Barros N de, Melo CFE, Gonzaga A, Vieira MA da C. A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images [Internet]. Proceedings of SPIE. 2017 ; 10134[citado 2024 jun. 18 ] Available from: https://doi.org/10.1117/12.2255516

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