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Computer Vision involves developing algorithms and models to enable computers to interpret and understand visual data from the world, mimicking human vision's ability to recognize, identify, and process information from images and videos. It brings together elements of mathematics, particularly linear algebra, probability, and calculus, as well as machine learning techniques like deep learning to analyze and make sense of visual content. This field is essential in computational science since it allows for automating tasks that would typically require human intervention, such as image-based medical diagnosis or autonomous vehicle navigation. By bridging the gap between data acquisition through sensors and high-level decision-making processes, Computer Vision contributes significantly to various applications in robotics, healthcare, security, and entertainment industries, ultimately advancing the broader fields of Science and Mathematics.