SIMULATION OF COMPUTER VISION SYSTEMS WITH ARTIFICIAL INTELLIGENCE

. In recent years, the rapid development of artificial intelligence technologies has led to significant advances in the field of computer vision, dedicated to extracting valuable information from visual data. This article presents a comprehensive analysis of computer vision systems modeling, focusing on the integration of AI techniques to enhance their performance and capabilities. Through an extensive literature review, this research investigates the latest methodologies, algorithms, and architectures employed in computer vision systems to accomplish tasks such as object detection, recognition, tracking, and segmentation. The article first provides an overview of computer vision systems and their significance in various domains, including autonomous vehicles, surveillance, robotics, and medical imaging. It then delves into the advancements of AI, particularly deep learning techniques such as convolutional neural networks and recurrent neural networks, which have revolutionized the field of computer vision. The article highlights the effectiveness of AI models in improving the accuracy and efficiency of computer vision tasks, showcasing their superiority over traditional image processing techniques. Furthermore, this research explores the challenges and limitations faced in modeling computer vision systems with AI. Modeling of computer vision systems with elements of artificial intelligence is carried out in the Matlab/Simulink. Issues such as dataset bias, scalability, interpretability, and robustness are discussed, along with proposed solutions and ongoing research efforts. Finally, the article concludes with a comprehensive outlook on the future of computer vision systems modeling with AI.