The monograph is devoted to the development of computer vision methods and tools using AI for fast identification of moving objects in a video data stream based on deep learning technologies. Classical and non-classical methods of artificial intelligence, convolutional neural networks, computer vision and image recognition, and theories of control systems based on estimates and criteria of mathematical statistics are considered. As a result of recognition, the type of recognised object is determined, and quantitative accuracy estimates are calculated. A method of applying templates is implemented. The algorithm has information about what the sought object looks like, what background it may have, what specific contours of the object look like and in what position they are. The possible location of the object detection is immediately taken into account. It allows for the achievement of high recognition quality and good performance. When a video camera shoots several similar objects, different templates are satisfied, and recognition decreases. Artificial neural network models are used to estimate or approximate functions that may depend on many inputs and are usually unknown.
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Victoria Vysotska is professor at Information Systems and Networks Department of Lviv Polytechnic National University. Lyubomyr Chyrun is associate professor at Applied Mathematics Department of Ivan Franko National University of Lviv. Oleksandr Lavrut is professor at Tactics Department of the Hetman Petro Sahaidachnyi National Army Academy.
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Taschenbuch. Zustand: Neu. Computer vision based on machine learning technology: monograph | Information technology for computer vision based on neural networks, artificial intelligence, video/image recognition | Victoria Vysotska (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786204983189 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Artikel-Nr. 131153920
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