Systematic review of state-of-the-art applications in the field of computer vision

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Paulo César Torres Abril
Santiago David Jara Moya
Leonardo David Torres Valverde
Darwin René Arias Martínez


Introduction:  Computer vision combines artificial intelligence and robotics to analyze images captured by cameras. It is based on the theory of RGB color perception and considers factors such as illumination and sensor type. It uses color models to accurately modify images. OpenCV and Python are used in this research on advanced techniques in computer vision, focusing on innovation and algorithms to improve object classification accuracy using machine learning and neural networks. Objective:  The main objective of this study is to conduct a comprehensive review of the available information on recent advances in machine vision by means of meta-analysis or systematic review, to address research more accurately in this field. Methodology:  The research focuses on computer vision, prioritizing recent scientific sources in English, although reliable books and web sources are included to a lesser extent. A qualitative approach is used through the Systematic Literature Review (SLR) methodology, which encompasses the formulation of questions, exploration of documents, rigorous selection of works and acquisition of relevant data. Results:  The analysis highlights that computer vision is an advanced field with diverse applications in intelligent device systems; a keyword analysis was also performed to identify key trends in the selected articles. Conclusion:  Most of the relevant studies on the subject were found in English databases such as IEEE and Springer, with limited references in Scopus due to their associated costs; the focus of this study is on intelligent systems and their application in real-time object detection using convolutional neural networks.


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Torres Abril , P. C., Jara Moya, S. D., Torres Valverde, L. D., & Arias Martínez, D. R. (2023). Systematic review of state-of-the-art applications in the field of computer vision. Ciencia Digital, 7(4), 26-53.


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