Computer Vision and AI: Bridging the Gap between Perception and Understanding in Machines

Abstract

The study of giving computers the ability to "see" is known as "computer vision." The overarching goal of computer vision problems is to draw some kind of conclusion about the real world from raw data about images. It spans several fields and disciplines, but can be roughly placed in the realm of AI and ML, where both specialized and more generalized learning approaches find application. Because it draws from many different branches of engineering and computer science, interdisciplinary research can sometimes give the impression of being chaotic. One vision problem might be easily resolved with a custom-built statistical approach, while another might require a complex and extensive collection of off-the-shelf machine learning techniques. The field of computer vision is at the forefront of modern research. Computer Vision and AI work together to enable machines to see and understand the visual world. Computer Vision focuses on developing algorithms and techniques that allow computers to extract meaningful information from images or videos. Conversely, artificial intelligence entails the development of smart machines with enhanced cognitive abilities. By combining these fields, we can create powerful systems that can recognize objects, understand scenes, and even interpret human emotions. Computer Vision and AI have the potential to revolutionize industries like healthcare, transportation, and entertainment, making our lives more convenient, efficient, and enjoyable.