FUNDAMENTOS DA VISÃO COMPUTACIONAL: ARCABOUÇO TEÓRICO DO RECONHECIMENTO ARTIFICIAL DE IMAGENS E VÍDEOS
Abstract
Este trabalho visa compreender os aspectos da visão computacional sob o prisma teórico. Na contemporaneidade, tal tecnologia é aplicada em diversos segmentos, desde o agronegócio até os serviços relacionados à saúde. Ademais, em consonância às recentes pesquisas, a utilização de mecanismos de visão computacional, em nível mundial, cresce consideravelmente. Tendo em vista essa particularidade, este artigo tem por objetivo central delinear os fundamentos da visão computacional por meio de arcabouço teórico acerca do reconhecimento artificial de imagens e vídeos. Para tanto, classifica-se o presente estudo em pesquisa básica. No que concerne ao processo de coleta de informações, incorporou-se a realização de uma pesquisa bibliográfica por intermédio de revisão de literatura. Nessa seara, destaca-se que foram considerados, especialmente, artigos científicos e livros oriundos das bases de dados Web of Science e Scopus. Em termos de resultados, contribuiu-se com um arcabouço conceitual sistematizado da tecnologia ora discutida.
References
ADAMIAN, N; et al. An Open-Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy. Laryngoscope, 00:1–7, 2020. DOI: 10.1002/lary.28669.
AGGARWAL, Charu C. Neural Networks and Deep Learning. NY, USA: Springer, 2018.
ALMEIDA, Dyego de Oliveira. Métodos de Visão Computacional Aplicados a Extração de Características de Ambientes Urbanos em Imagens de Satélite de Baixa Resolução. Dissertação de Mestrado em Direito Programa de Pós–Graduação do Instituto de Informática da Universidade Federal de Goiás, Goiânia, 2018.
BHATT, Dulari; et al. CNN Variants for Computer Vision: History, Architecture,
Application, Challenges and Future Scope. Electronics, 2021, 10, 2470. DOI: 10.3390/electronics10202470.
CERTI. Visão computacional: conheça as aplicações e a importância para a transformação digital. CERTI, 2020. Disponível em: < https://certi.org.br/blog/visao-computacional/>. Acesso em: 20 jul. 2022.
CHOI, Rene Y.; et al. Introduction to Machine Learning, Neural Networks, and Deep Learning. TVST, Special Issue, Vol. 9, Nº 2. DOI: 10.1167/tvst.9.2.14.
CORREIA, David L. P.; et al. Leveraging Artificial Intelligence for Large-Scale Plant Phenology Studies From Noisy Time-Lapse Images. IEEE Access, Vol. 8, 2020.
COSTA, Lucas; et al. Determining leaf stomatal properties in citrus trees utilizing machine vision and artifcial intelligence. Precision Agriculture, 22, 2021. DOI: 10.1007/s11119-020-09771-x.
COZMAN, Fabio G.; NERI, Hugo. O que, afinal, é Inteligência Artificial? In: COZMAN, Fabio G.; PLONSKI, Guilherme Ary; NERI, Hugo (Org.). Inteligência artificial: avanços e tendências. São Paulo: Instituto de Estudos Avançados, 2021.
DAVIES, E. R. Computer and Machine Vision: Theory, Algorithms, Practicalities. 4º Ed. Waltham, USA: Elsevier/AP, 2012.
FORSYTH, David A.; PONCE, Jean. Computer Vision: a Modern Approach. 2ª Ed. Boston, USA: Pearson, 2012.
GONZALEZ, Rafael C.; WOODS, Richard E. Digital Image Processing. 4ª Ed. New York, USA: Pearson, 2018.
GONZALEZ, Rafael C.; WOODS, Richard E. Digital Image Processing. 3ª Ed. New York, USA: Pearson, 2009.
GUEDES, Marcelo Santiago; MACHADO, Henrique Felix de Souza. Veículos Autônomos Inteligentes e a Responsabilidade Civil nos Acidentes de Trânsito no Brasil: Desafios regulatórios e propostas de solução e regulação. V. 2. Brasília: ESMPU, 2020.
HOLM, Elizabeth A.; et al. Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis. Metallurgical And Materials Transactions A, 2020. DOI: 10.1007/s11661-020-06008-4.
HOLZINGER, Andreas. Introduction to MAchine Learning & Knowledge Extraction (MAKE). Mach. Learn. Knowl. Extr. 2018, 1, 1. DOI:10.3390/make101000.
HOSSEINI, Kasra; et al. Maps of a Nation? The Digitized Ordnance Survey for New Historical Research. Journal of Victorian Culture, 2021, Vol. 26, No. 2, 284–299. DOI: 10.1093/jvcult/vcab009.
JANAI, Joel; et. al. Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art. Foundations and Trends in Computer Graphics and Vision. Vol. 12, No. 1–3, pp 1–308. DOI: 10.1561/0600000079.
JOSHI, Khushbu; PATEL, Manish I. Recent advances in local feature detector and descriptor: a literature survey. International Journal of Multimedia Information Retrieval, 2020. DOI: 10.1007/s13735-020-00200-3.
KHEMASUWAN, Danai; et al. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir Rev 2020; 29: 200181. DOI: 10.1183/16000617.0181-2020.
LI, Yu; et al. Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks. Accident Analysis and Prevention, 151, 2021, 105962. DOI: 10.1016/j.aap.2020.105962.
LUDEMIR, Teresa Bernarda. Inteligência Artificial e Aprendizado de Máquina: estado atual e tendências. Estudos Avançados, 35 (101), 2021.
MUTASA, Simukayi; SUN, Shawn; HA, Richard. Understanding artificial intelligence based radiology studies: CNN architecture. Clinical Imaging 80, 2021, 72–76. DOI: 10.1016/j.clinimag.2021.06.033.
NGOC, Hung; et al. Automatic Facial Expression Recognition System Using Convolutional Neural Networks. V. Van Toi et al. (eds.), 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7), IFMBE Proceedings. DOI: 10.1007/978-981-13-5859-3_82.
NIXON, Mark S.; AGUADO, Alberto S. Feature Extraction and Image Processing for Computer Vision. 4ª Ed. London, UK: Academic Press/ Elsevier, 2020.
NVIDIA. Mercedes-Benz and NVIDIA to Build Software-Defined Computing Architecture for Automated Driving Across Future Fleet. NVIDIA, 2020. Disponível em: < https://nvidianews.nvidia.com/news/mercedes-benz-and-nvidiato-build-software-defined-computing-architecture-for-automated-driving-across-future-fleet>. Acesso em: 20 jul. 2022.
PALSULE, Siddhesh; MISHRA, Sandeep. Computer Vision Market Analysis and Segment Forecasts to 2028. Grand View Research, 2020.
PANGAL, Dhiraj J.; et al. A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision. World Neurosurgery, 2021. DOI: 10.1016/j.wneu.2021.03.022.
PEREIRA, Gabriel; MORESCHI, Bruno. Artificial intelligence and institutional critique 2.0: unexpected ways of seeing with computer vision. AI & Society, 2020. DOI: 10.1007/s00146-020-01059-y.
PEREIRA, Gabriel; MORESCHI, Bruno. Artificial intelligence and institutional critique 2.0: unexpected ways of seeing with computer vision. AI & Society, 2020. DOI: 10.1007/s00146-020-01059-y.
PRATEEK, Joshi. Artificial Intelligence with Python: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Mumbai, India: Packt, 2017.
ROBERTS, Lawrence Gilman. Machine Percepton of Three-dimensional Solids. Tese de doutorado, Massachusetts Institute of Technology, 1961.
RUSSELL, Stuart J.; NORVIG, Peter. Inteligência Artificial. 3ª Ed. Tradução: SIMILLE, Regina Célia. Rio de Janeiro: Elsevier, 2013.
RUSSELL, Stuart J.; NORVIG, Peter. Inteligência Artificial. 4ª Ed. Hoboken: Pearson, 2021.
SAVEKAR, Avinash; KUMAR, Vineet. AI in Computer Vision Market: global opportunity analysis and industry forecast, 2021-2030. Portland: Allied Market Research, 2021.
SEBE, N.; et al. Machine Learning in Computer Vision. Dordrecht, Netherlands: Springer, 2005.
SHANMUGAMANI, Rajalingappaa. Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using Tensor Flow and Keras. Mumbai, India: Packt, 2018.
SHEAVES, Marcus; et al. Optimizing video sampling for juvenile fish surveys: Using deep learning and evaluation of assumptions to produce critical fisheries parameters. Fish and Fisheries, 2020, 1–18. DOI: 10.1111/faf.12501.
SIMÃO, Bárbara; FRAGOSO, Nathalie; ROBERTO, Enrico. Reconhecimento Facial e o Setor Privado: guia para a adoção de boas práticas. InternetLab/IDEC, São Paulo, 2020.
SMITS, Thomas; WEVERS, Melvin. The agency of computer vision models as optical instruments. Visual Communication, Vol. 0(0) 1–21, 2021. DOI: 10.1177/1470357221992097.
SMITS, Thomas; WEVERS, Melvin. The agency of computer vision models as optical instruments. Visual Communication, Vol. 0(0) 1–21, 2021. DOI: 10.1177/1470357221992097.
SONKA, Milan; HLAVÁČ, Vaclav; BOYLE, Roger. Image Processing, Analysis, and Machine Vision. 4ª Ed. Stamford, USA: Cengage Learning, 2014.
TAULLI, Tom. Artificial Intelligence Basics: a non-technical introduction. Monrovia, USA: Apress, 2019.
VENTURUS. Visão Computacional. Venturus, 2022.
AGGARWAL, Charu C. Neural Networks and Deep Learning. NY, USA: Springer, 2018.
ALMEIDA, Dyego de Oliveira. Métodos de Visão Computacional Aplicados a Extração de Características de Ambientes Urbanos em Imagens de Satélite de Baixa Resolução. Dissertação de Mestrado em Direito Programa de Pós–Graduação do Instituto de Informática da Universidade Federal de Goiás, Goiânia, 2018.
BHATT, Dulari; et al. CNN Variants for Computer Vision: History, Architecture,
Application, Challenges and Future Scope. Electronics, 2021, 10, 2470. DOI: 10.3390/electronics10202470.
CERTI. Visão computacional: conheça as aplicações e a importância para a transformação digital. CERTI, 2020. Disponível em: < https://certi.org.br/blog/visao-computacional/>. Acesso em: 20 jul. 2022.
CHOI, Rene Y.; et al. Introduction to Machine Learning, Neural Networks, and Deep Learning. TVST, Special Issue, Vol. 9, Nº 2. DOI: 10.1167/tvst.9.2.14.
CORREIA, David L. P.; et al. Leveraging Artificial Intelligence for Large-Scale Plant Phenology Studies From Noisy Time-Lapse Images. IEEE Access, Vol. 8, 2020.
COSTA, Lucas; et al. Determining leaf stomatal properties in citrus trees utilizing machine vision and artifcial intelligence. Precision Agriculture, 22, 2021. DOI: 10.1007/s11119-020-09771-x.
COZMAN, Fabio G.; NERI, Hugo. O que, afinal, é Inteligência Artificial? In: COZMAN, Fabio G.; PLONSKI, Guilherme Ary; NERI, Hugo (Org.). Inteligência artificial: avanços e tendências. São Paulo: Instituto de Estudos Avançados, 2021.
DAVIES, E. R. Computer and Machine Vision: Theory, Algorithms, Practicalities. 4º Ed. Waltham, USA: Elsevier/AP, 2012.
FORSYTH, David A.; PONCE, Jean. Computer Vision: a Modern Approach. 2ª Ed. Boston, USA: Pearson, 2012.
GONZALEZ, Rafael C.; WOODS, Richard E. Digital Image Processing. 4ª Ed. New York, USA: Pearson, 2018.
GONZALEZ, Rafael C.; WOODS, Richard E. Digital Image Processing. 3ª Ed. New York, USA: Pearson, 2009.
GUEDES, Marcelo Santiago; MACHADO, Henrique Felix de Souza. Veículos Autônomos Inteligentes e a Responsabilidade Civil nos Acidentes de Trânsito no Brasil: Desafios regulatórios e propostas de solução e regulação. V. 2. Brasília: ESMPU, 2020.
HOLM, Elizabeth A.; et al. Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis. Metallurgical And Materials Transactions A, 2020. DOI: 10.1007/s11661-020-06008-4.
HOLZINGER, Andreas. Introduction to MAchine Learning & Knowledge Extraction (MAKE). Mach. Learn. Knowl. Extr. 2018, 1, 1. DOI:10.3390/make101000.
HOSSEINI, Kasra; et al. Maps of a Nation? The Digitized Ordnance Survey for New Historical Research. Journal of Victorian Culture, 2021, Vol. 26, No. 2, 284–299. DOI: 10.1093/jvcult/vcab009.
JANAI, Joel; et. al. Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art. Foundations and Trends in Computer Graphics and Vision. Vol. 12, No. 1–3, pp 1–308. DOI: 10.1561/0600000079.
JOSHI, Khushbu; PATEL, Manish I. Recent advances in local feature detector and descriptor: a literature survey. International Journal of Multimedia Information Retrieval, 2020. DOI: 10.1007/s13735-020-00200-3.
KHEMASUWAN, Danai; et al. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir Rev 2020; 29: 200181. DOI: 10.1183/16000617.0181-2020.
LI, Yu; et al. Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks. Accident Analysis and Prevention, 151, 2021, 105962. DOI: 10.1016/j.aap.2020.105962.
LUDEMIR, Teresa Bernarda. Inteligência Artificial e Aprendizado de Máquina: estado atual e tendências. Estudos Avançados, 35 (101), 2021.
MUTASA, Simukayi; SUN, Shawn; HA, Richard. Understanding artificial intelligence based radiology studies: CNN architecture. Clinical Imaging 80, 2021, 72–76. DOI: 10.1016/j.clinimag.2021.06.033.
NGOC, Hung; et al. Automatic Facial Expression Recognition System Using Convolutional Neural Networks. V. Van Toi et al. (eds.), 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7), IFMBE Proceedings. DOI: 10.1007/978-981-13-5859-3_82.
NIXON, Mark S.; AGUADO, Alberto S. Feature Extraction and Image Processing for Computer Vision. 4ª Ed. London, UK: Academic Press/ Elsevier, 2020.
NVIDIA. Mercedes-Benz and NVIDIA to Build Software-Defined Computing Architecture for Automated Driving Across Future Fleet. NVIDIA, 2020. Disponível em: < https://nvidianews.nvidia.com/news/mercedes-benz-and-nvidiato-build-software-defined-computing-architecture-for-automated-driving-across-future-fleet>. Acesso em: 20 jul. 2022.
PALSULE, Siddhesh; MISHRA, Sandeep. Computer Vision Market Analysis and Segment Forecasts to 2028. Grand View Research, 2020.
PANGAL, Dhiraj J.; et al. A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision. World Neurosurgery, 2021. DOI: 10.1016/j.wneu.2021.03.022.
PEREIRA, Gabriel; MORESCHI, Bruno. Artificial intelligence and institutional critique 2.0: unexpected ways of seeing with computer vision. AI & Society, 2020. DOI: 10.1007/s00146-020-01059-y.
PEREIRA, Gabriel; MORESCHI, Bruno. Artificial intelligence and institutional critique 2.0: unexpected ways of seeing with computer vision. AI & Society, 2020. DOI: 10.1007/s00146-020-01059-y.
PRATEEK, Joshi. Artificial Intelligence with Python: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Mumbai, India: Packt, 2017.
ROBERTS, Lawrence Gilman. Machine Percepton of Three-dimensional Solids. Tese de doutorado, Massachusetts Institute of Technology, 1961.
RUSSELL, Stuart J.; NORVIG, Peter. Inteligência Artificial. 3ª Ed. Tradução: SIMILLE, Regina Célia. Rio de Janeiro: Elsevier, 2013.
RUSSELL, Stuart J.; NORVIG, Peter. Inteligência Artificial. 4ª Ed. Hoboken: Pearson, 2021.
SAVEKAR, Avinash; KUMAR, Vineet. AI in Computer Vision Market: global opportunity analysis and industry forecast, 2021-2030. Portland: Allied Market Research, 2021.
SEBE, N.; et al. Machine Learning in Computer Vision. Dordrecht, Netherlands: Springer, 2005.
SHANMUGAMANI, Rajalingappaa. Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using Tensor Flow and Keras. Mumbai, India: Packt, 2018.
SHEAVES, Marcus; et al. Optimizing video sampling for juvenile fish surveys: Using deep learning and evaluation of assumptions to produce critical fisheries parameters. Fish and Fisheries, 2020, 1–18. DOI: 10.1111/faf.12501.
SIMÃO, Bárbara; FRAGOSO, Nathalie; ROBERTO, Enrico. Reconhecimento Facial e o Setor Privado: guia para a adoção de boas práticas. InternetLab/IDEC, São Paulo, 2020.
SMITS, Thomas; WEVERS, Melvin. The agency of computer vision models as optical instruments. Visual Communication, Vol. 0(0) 1–21, 2021. DOI: 10.1177/1470357221992097.
SMITS, Thomas; WEVERS, Melvin. The agency of computer vision models as optical instruments. Visual Communication, Vol. 0(0) 1–21, 2021. DOI: 10.1177/1470357221992097.
SONKA, Milan; HLAVÁČ, Vaclav; BOYLE, Roger. Image Processing, Analysis, and Machine Vision. 4ª Ed. Stamford, USA: Cengage Learning, 2014.
TAULLI, Tom. Artificial Intelligence Basics: a non-technical introduction. Monrovia, USA: Apress, 2019.
VENTURUS. Visão Computacional. Venturus, 2022.
Published
2024-06-17
Section
FLUXO CONTÍNUO - Artigos
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