Systematic review of state-of-the-art applications in the field of computer vision
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Publicación 2. Vol 7. Num 4 (Español (España))
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Published:
Oct 19, 2023
Keywords:
Algorithms, Artificial, Deep Learning, SLR Methodology, Image Processing, Vision.
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Abstract
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. https://doi.org/10.33262/cienciadigital.v7i4.2710
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References
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Kusuma, S., Udayan, J. D., & Sachdeva, A. (2019). Driver distraction detection using deep learning and computer vision. 2nd International Conference on Intelligent Computing, 1(1), 289-292.
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Mantegazza, D., Guzzi, J., Gambardella, L. M., & Giusti, A. (2019). Learning Vision-Based Quadrotor Control in User Proximity. 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 1(1), 369-369.
Mohanasundaram, S., Krishnan, V., & Madhubala, V. (2019). Vehicle Theft Tracking, Detecting and Locking System Using Open CV. 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 1(1), 1075-1078.
Moreno Ligero, M., Lucena Anton, D., Salazar, A., Failde, I., & Moral Muñoz, J. A. (2023). mHealth Impact on Gait and Dynamic Balance Outcomes in Neurorehabilitation: Systematic Review and Meta-analysis. Journal of Medical Systems, 47(1), 1-19.
Mostafi, S., Zhao, W., Sukreep, S., Elgazzar, K., & Azim, A. (2022). Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 1(1), 1164-1168.
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access, 7(1), 19143-19165.
O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., & Hernandez, G. V. (2020). Deep Learning vs. Traditional Computer Vision. Advances in Intelligent Systems and Computing, 943(105), 128-144.
Pavithra, R., & Suresh, K. (2019). Fingerprint Image Identification for Crime Detection. International Conference on Communication and Signal Processing (ICCSP), 1(1), 0797-0800.
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Yu, L., Sun, W., Wang, H., Wang, Q., & Liu, C. (2018). The Design of Single Moving Object Detection and Recognition System Based on OpenCV. IEEE International Conference on Mechatronics and Automation (ICMA), 1(1), 1163-1168.
Yudin, D., Sotnikov, A., & Krishtopik, A. (2019). Detection of Big Animals on Images with Road Scenes using Deep Learning. Proceedings - 2019 International Conference on Artificial Intelligence: Applications and Innovations, IC-AIAI, 1(1), 100-103.
Auysakul, J., Xu, H., & Pooneeth, V. (2019). Development of Multi-process for Video Stitching in the AVM Applications Based on OpenCV. Proceedings of International Conference on Mechatronics and Intelligent Robotics, 1(1), 1013-1020.
Bellemo, V., Lim, Z. W., Lim, G., Nguyen, Q. D., Xie, Y., Yip, M. Y., &. Tan, G. S. (2019). Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. The Lancet Digital Health, 1(1), 35-44.
Berjon, D., Carballeira, P., Cabrera, J., Carmona, C., Corregidor, D., Diaz, C., &. García, N. (2020). FVV Live: Real-Time, Low-Cost, Free Viewpoint Video. 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020, 1(1), 1-2.
Bhattacharya, T., & Chatterjee, A. (2017). Evaluating performance of some common filtering techniques for removal of Gaussian noise in images. 2017 IEEE International Conference on Power, Control, Signals, and Instrumentation Engineering (ICPCSI), 1(1), 1981–1984.
Buzzin, A., Asquini, R., Caputo, D., & De, G. (2019). Sensors and Image Processing. En Advances in Intelligent Systems and Computing (Vol. 651, pp. 137-142). Singapore: Springer Nature Singapore Pte Ltd. 2018.
Cadena Moreano, J. A., La Serna Palomino, N. B., & Llano Casa, A. C. (2019). Facial recognition techniques using SVM: A comparative analysis. Enfoque UTE, 3(98-111), 10.
Chandan, G., Jain, A., & Jain, H. (2021). Real Time Object Detection and Tracking Using Deep Learning and OpenCV. Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, 1(1), 1305-1308.
De Lima, R., Cabrera-Ponce, A. A., & Martinez-Carranza, J. (2021). Parallel hashing-based matching for real-time aerial image mosaicing. Journal of Real-Time Image Processing, 18(1), 143-156.
Deep, S., & Zheng, X. (2019). Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition. 29th International Telecommunication Networks and Applications Conference (ITNAC), 1(1), 1-4.
Díaz-Toro, A. A., Paz Pérez, L. M., Piniés Rodríguez, P., & Caicedo Bravo, E. F. (2018). Dense tracking, mapping and scene labeling using a depth camera. Revista Facultad de Ingeniería Universidad de Antioqui, 86(1), 54-69.
Estarita, J., Jim, A. B., J., E., & Moreno, S. (2017). Sistema de Reconocimiento de objetos en tiempo real. Investigación y desarrollo en TIC, 8(2), 41-45.
Gadi, V. K., Alybaev, D., Raj, P., Garg, A., Mei, G., Sreedeep, S., & Sahoo, L. (2020). A Novel Python Program to Automate Soil Color Analysis and Interpret Surface Moisture Content. International Journal of Geosynthetics and Ground Engineering, 6(2), 1-8.
Guo, Z., Wu, G., Shi, X., Sui, M., Song, X., Xu, Y., & Shibasaki, R. (2019). Geosr: A Computer Vision Package for Deep Learning Based Single-Frame Remote Sensing Imagery Super-Resolution. International Geoscience and Remote Sensing Symposium, 1(1), 3376-3379.
Harikrishnan, J., Sudarsan, A., Sadashiv, A., & Ajai, R. A. (2019). Vision-face recognition attendance monitoring system for surveillance using deep learning technology and computer vision. International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 1(1), 1-5.
Ho, P. T., Albajez, J. A., Santolaria, J., & Yagüe-Fabra, J. A. (2022). Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review. Applied Sciences (Switzerland), 12(4), 1-51.
Jain, B. D., Thakur, S. M., & Suresh, K. V. (2018). Visual Assistance for Blind Using Image Processing. International Conference on Communication and Signal Processing (ICCSP), 1(1), 0499-0503.
Johnston, B., & Chazal, P. (2018). A review of image-based automatic facial landmark identification techniques. Eurasip Journal on Image and Video Processing, 1(1), 1-23.
Khaliluzzaman, M., Yakub, M., & Chakraborty, N. (2018). Comparative Analysis of Stairways Detection Based on RGB and RGB-D Image. International Conference on Innovations in Science, Engineering and Technology (ICISET), 1(1), 519–524.
Khuushi, J. V., Patrikar, R., & Deshmukh, R. (2018). Real Time Mixing Index Measurement of Microchannels Using OpenCV. In VLSI Design and Test: 22nd International Symposium, 1(1), 278-284.
Kulkarni, B. P., Krishna, S. S., Meenakshi, K., Kora, P., & Swaraja, K. (2020). Performance Analysis of Optimization Algorithms GA, PSO, and ABC based on DWT-SVD watermarking in OpenCV Python Environment, 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 1(1), 1-5.
Kusuma, S., Udayan, J. D., & Sachdeva, A. (2019). Driver distraction detection using deep learning and computer vision. 2nd International Conference on Intelligent Computing, 1(1), 289-292.
Lee, H. L., Kang, J., Lim, J., Kim, S. C., Jeon, S. O., & Lee, J. Y. (2023). Hybridization of short-range and long-range charge transfer excited states in multiple resonance emitter. Nature communications, 14(1), 1-8.
Manju, A., & Valarmathie, P. (2021). Video analytics for semantic substance extraction using OpenCV in python. Journal of Ambient Intelligence and Humanized Computing, 12(3), 4057-4066.
Mantegazza, D., Guzzi, J., Gambardella, L. M., & Giusti, A. (2019). Learning Vision-Based Quadrotor Control in User Proximity. 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 1(1), 369-369.
Mohanasundaram, S., Krishnan, V., & Madhubala, V. (2019). Vehicle Theft Tracking, Detecting and Locking System Using Open CV. 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 1(1), 1075-1078.
Moreno Ligero, M., Lucena Anton, D., Salazar, A., Failde, I., & Moral Muñoz, J. A. (2023). mHealth Impact on Gait and Dynamic Balance Outcomes in Neurorehabilitation: Systematic Review and Meta-analysis. Journal of Medical Systems, 47(1), 1-19.
Mostafi, S., Zhao, W., Sukreep, S., Elgazzar, K., & Azim, A. (2022). Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 1(1), 1164-1168.
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access, 7(1), 19143-19165.
O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., & Hernandez, G. V. (2020). Deep Learning vs. Traditional Computer Vision. Advances in Intelligent Systems and Computing, 943(105), 128-144.
Pavithra, R., & Suresh, K. (2019). Fingerprint Image Identification for Crime Detection. International Conference on Communication and Signal Processing (ICCSP), 1(1), 0797-0800.
Rao, T. V., Gaddam, A., Kurni, M., & Saritha, K. (2021). Reliance on artificial intelligence, machine learning and deep learning in the era of industry 4.0. Smart Healthcare System Design: Security and Privacy Aspects, 1(1), 281-300.
Rodríguez, A., Rabuñal, J. R., Bermudez, M., & Puertas, J. (2015). Detection of fishes in turbulent waters based on image analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7931(2), 404-412.
Sravya, C., Niharika, C. S., Sai, D., & Reddy, D. R. (2021). Turkish Journal of Computer and Mathematics Education Fingerprint Image Recognition for Crime Detection Research Article Turkish Journal of Computer and Mathematics Education Research Article. Computer and Mathematics Education, 12(12), 2230-2237.
Sánchez Meca, J., Sánchez, J., & Estrada Lorenzo, J. (2010). Cómo realizar una revisión sistemática. REDINED - Red de Información Educativa, 38(1666), 53-64.
Sasaki, H., Horiuchi, T., & Kato, S. (2017). A study on vision-based mobile robot learning by deep Q-network. 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 1(1), 799-804.
Shubham, M., Verma, V., Akhtar, N., Chaturvedi, S., & Perwej, Y. (2022). An Intelligent Motion Detection Using OpenCV. International Journal of Scientific Research in Science, Engineering and Technology, 4099(1), 51-63.
Sriratana, W., Mukma, S., Tammarugwattana, N., & Sirisantisamrid, K. (2018). Application of the OpenCV-Python for Personal Identifier Statement. 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 1(1), 1-4.
Swain, M., Dhariwal, S., & Kumar, G. (2018). A Python (Open CV) Based Automatic Tool for Parasitemia Calculation in Peripheral Blood Smear. Proceedings - 2nd International Conference on Intelligent Circuits and Systems, ICICS 2018, 1(1), 445-448.
Xia, Y., Nie, B., Zhang, Y., Wang, Z., Wang, Z., Liu, S., & Zhang, B. (2020). Design and implementation of tunnel image mosaic system based on open CV. International Journal of System Assurance Engineering and Management, 11(4), 792-797.
Yu, L., Sun, W., Wang, H., Wang, Q., & Liu, C. (2018). The Design of Single Moving Object Detection and Recognition System Based on OpenCV. IEEE International Conference on Mechatronics and Automation (ICMA), 1(1), 1163-1168.
Yudin, D., Sotnikov, A., & Krishtopik, A. (2019). Detection of Big Animals on Images with Road Scenes using Deep Learning. Proceedings - 2019 International Conference on Artificial Intelligence: Applications and Innovations, IC-AIAI, 1(1), 100-103.